IEEE 30-bus Power System Research Articles

A stochastic programming approach using multiple uncertainty sets for AC robust transmission expansion planning

We present a transmission expansion planning model to find the most desirable configuration of transmission grid under the robust optimization. Unlike the common existing approaches where only one uncertainty set is considered, we propose to model multiple uncertainty sets each with its own probability. In this regard, we devise a method to construct the corresponding uncertainty sets from the historical data based on minimum covering circle. Moreover, we introduce a new criterion for uncertainty budget based on which the planner can efficiently control the conservative level of the optimization problem. Using this new criterion, the planner is capable to make a trade-off between the tractability and solution quality. The robust planning method is formulated as a tri-level min–max–min optimization model in which the classic column-and-constraint generation technique is used to solve the problem. The proposed strategy is implemented on the IEEE 118-bus power system to show the effectiveness of the model. The results indicate the short-term uncertainties in demand and intermittent renewable generation can be efficiently captured by the presented strategy. The comparable results also corroborate the superiority of the model over the single uncertainty-set-based models as well as over the pure stochastic models. • A robust transmission expansion planning scheme is presented. • Short-term demand and wind speed uncertainties are taken into account. • Multiple uncertainty sets are considered rather than a single one. • A linearized network model is utilized. • The robust trilevel approach is solved using KKT and CCG.

Transmission Expansion Planning Considering Resistance Variations of Overhead Lines Using Minimum-Volume Covering Ellipsoid

Reliable planning of electricity networks is a crucial challenge to maintain the continuous power supply. This can be ensured through careful planning which requires a number of parameters as inputs such as electricity demand, generation capacity, fuel price, renewable intermittent generation, among others. The uncertain behaviors of such parameters have been well studied in literature. Another important parameter which is a vital requirement for reliable planning is the resistance of overhead transmission lines. Traditionally, the current models ignore to consider the resistance variations due to Joule heating and ambient temperature changes in transmission expansion planning (TEP) problem. In this sense, this paper presents an adaptive robust optimization (ARO) framework for TEP to model the uncertain resistance through ellipsoidal uncertainty set. Moreover, a data-driven selection of the ellipsoidal uncertainty set is proposed. In this regard, Khachiyans algorithm (KA) is used to identify the minimum-volume covering ellipsoid (MVCE) that contains all uncertain parameters. A case study based on IEEE 118-bus power system is presented to demonstrate the effectiveness of the proposed method. Simulation results show that resistance uncertainty is of serious concern in the TEP problem since the solutions are highly sensitive to fluctuations in the line resistances.

Economic-emission dispatch problem in integrated electricity and heat system considering multi-energy demand response and carbon capture Technologies

To combat global warming caused by excessive CO2 emissions, it is critical to achieving low-carbon economic operation of integrated electricity and heat systems (IEHS). Thermal power plants with carbon capture systems can significantly reduce the spread of carbon emissions, thus effectively reducing the overall carbon footprint. Meanwhile, the development and utilization of renewable energy sources, such as wind power, can effectively limit overall carbon emissions. Hence, this paper proposes a multi-objective optimization scheme incorporating carbon capture power plants (CCPP) and multi-energy demand response (MEDR), considering the uncertainties of the system for the economic-emission dispatch problem in IEHS. First, the energy flow mechanism and analysis of the wind power abandonment of the IEHS are presented. Then, the low-carbon mechanisms of the MEDR and CCPP are investigated. The uncertainties of the system are described by a membership function to ensure the reserve requirements. A weight setting method based on the improved analytic hierarchy process method is proposed, in which the multi-objective function is transformed into a single-objective function. The case studies were tested on an improved IEEE-30 bus power system and a 6-bus district heating system. The results demonstrate the effectiveness of the proposed method in reducing carbon emissions and improving wind power consumption.

Power system frequency stability using optimal sizing and placement of Battery Energy Storage System under uncertainty

In the literature, a variety of system uncertainty is not included in the frequency stability research. This research demonstrated that employing the probabilistic frequency stability analysis approach which accounts for the uncertainty of system parameters such as loads and generation uncertainty, resulted in a more accurate frequency stability assessment when compared to the deterministic technique. Additionally, this study proposes a framework for determining the optimal size and placement of Battery Energy Storage System (BESS) in power systems while taking into account the uncertainty associated with system generation and demand, as well as intermittent generation sources and contingency locations which have been rarely considered in the literature. The novelty of these methods is that they determine the optimal BESS size and placement based on the amount and location of the critical system loads. The methodologies have been validated using IEEE 39-bus and simplified SE-Australian power systems where the simulations were implemented using DIgSILENT/PowerFactory software with Python interface. The simulation results revealed that the optimal BESS size should not be equal to more than 4.5% from the system loads amount while the optimal BESS placement was acquired when the BESS are placed closer to around 30% of the system loads. Furthermore, by investigating the impact of the contingency and wind farm locations on the optimal BESS size, the contingency location had the greatest impact on the BESS properties, while the wind farm locations provided a negligible impact.

Maximum Correntropy with Variable Center Unscented Kalman Filter for Robust Power System State Estimation.

The robust Kalman filter with correntropy loss has received much attention in recent years for forecasting-aided state estimation in power systems, since it efficiently reduces the negative influence of various abnormal situations, such as non-Gaussian communication, changing environment, and instrument failures, and obviously improves the stability of power systems. However, the existing correntropy-based robust Kalman filters usually use the Gaussian function with a fixed center as the kernel function in correntropy, which may not be a suitable choice in practical applications of power system forecasting-aided state estimation (PSSE). To address this issue, a new and robust unscented Kalman filter, called the maximum correntropy with variable center unscented Kalman filter (MCVUKF), is proposed in this paper for PSSE. Specifically, MCVUKF adopts an extended version of correntropy, whose center can be located at any position, to replace the original correntropy in an unscented Kalman filter to improve the performance in PSSE. Moreover, by using an exponential function of the innovation vector to adjust a covariance matrix, an enhanced MCVUKF (En-MCVUKF) method is also developed for suppressing the influence of bad data to the innovation vector and further improving the accuracy of PSSE. Finally, extensive simulations have been conducted on IEEE 14-bus, 30-bus, and 57-bus test power systems, and the simulation results have shown the superiority of the proposed MCVUKF and En-MCVUKF methods compared with several related state-of-the-art Kalman filter methods.

Mechanism Analysis of a Subsequent Commutation Failure and a DC Power Recovery Speed Control Strategy

A subsequent commutation failure (SCF) of the Line-Commutated Converter–High-Voltage Direct Current (LCC-HVDC) may occur during the recovery after the clearance of an AC fault, seriously threatening the safe and stable operation of the LCC-HVDC and the entire post-fault AC/DC hybrid power system. In this study, the mechanism of an SCF as affected by the transient stability was analyzed, and a DC power recovery speed control strategy is proposed as an additional form of control to prevent the occurrence of SCFs. First, the sending-end and receiving-end power systems were modeled as synchronous generators instead of ideal voltage sources, and the mechanism of an SCF as affected by the transient stability was analyzed and verified. Second, the ramp function was adopted to describe the recovery characteristic of DC power, and a model of its recovery was established. Then, the recovery speed control strategy is presented based on the mechanism analysis of SCFs, which can not only be used to avoid the occurrence of SCFs but also increase the transient stability margin of the sending-end power system. Finally, the effectiveness and robustness of the proposed control strategy were validated by using a hybrid electromechanical–electromagnetic model and the full electromagnetic model of the IEEE 39-bus asynchronous interconnection test power system. With the implementation of the proposed control strategy, the safe and stable operation of the LCC-HVDC and AC/DC hybrid power system can be guaranteed. The adaptive DC power recovery speed control strategy will be further investigated in future research work.

A Robust Scheduling Methodology for Integrated Electric-Gas System Considering Dynamics of Natural Gas Pipeline and Blending Hydrogen

As smart grid develops and renewables advance, challenges caused by uncertainties of renewables have been seriously threatening the energy system’s safe operation. Nowadays, the integrated electric-gas system (IEGS) plays a significant role in promoting the flexibility of modern grid owing to its great characteristic in accommodating renewable energy and coping with fluctuation and uncertainty of the system. And hydrogen, as an emerging and clean energy carrier, can further enhance the energy coupling of the IEGS and promote carbon neutralization with the development of power-to-hydrogen (P2H) technology and technology of blending hydrogen in the natural gas system. Dealing with the uncertainty of renewables, a robust schedule optimization model for the integrated electric and gas systems with blending hydrogen (IEGSH) considering the dynamics of gas is proposed and the iterative solving method based on column-and-constraint generation (C&CG) algorithm is implemented to solve the problem. Case studies on the IEGSH consisting of IEEE 39-bus power system and 27-node natural gas system validate the effectiveness of the dynamic energy flow model in depicting the transient process of gas transmission. The effectiveness of the proposed robust day-ahead scheduling model in dealing with the intra-day uncertainty of wind power is also verified. Additionally, the carbon emission reduction resulting from the blending of hydrogen is evaluated.

Equivalent Method for Nonreciprocal Networks Based on Sensitivity Consistency

An equivalent method for a nonreciprocal network is proposed. The method introduces controlled current sources into an equivalent network to preserve the nonreciprocal characteristics of the original network. The parameters of the equivalent network are derived under the condition of keeping the network sensitivities at the internal network and border nodes unchanged before and after equivalence. Compared to the existing equivalent methods, which are restricted to reciprocal networks and ignore the consistency of network sensitivities, the proposed method is more adaptable and accurate when applied in practical nonreciprocal networks, such as power systems. This method is applied to static security analysis of an IEEE 39-bus power system and a utility 661-bus power system to illustrate its effectiveness and accuracy in practical systems.

Optimal Reactive Power Dispatch Using a Chaotic Turbulent Flow of Water-Based Optimization Algorithm

In this study, an optimization algorithm called chaotic turbulent flow of water-based optimization (CTFWO) algorithm is proposed to find the optimal solution for the optimal reactive power dispatch (ORPD) problem. The ORPD is formulated as a complicated, mixed-integer nonlinear optimization problem, comprising control variables which are discrete and continuous. The CTFWO algorithm is used to minimize voltage deviation (VD) and real power loss (P_loss) for IEEE 30-bus and IEEE 57-bus power systems. These goals can be achieved by obtaining the optimized voltage values of the generator, the transformer tap changing positions, and the reactive compensation. In order to evaluate the ability of the proposed algorithm to obtain ORPD problem solutions, the results of the proposed CTFWO algorithm are compared with different algorithms, including artificial ecosystem-based optimization (AEO), the equilibrium optimizer (EO), the gradient-based optimizer (GBO), and the original turbulent flow of water-based optimization (TFWO) algorithm. These are also compared with the results of the evaluated performance of various methods that are used in many recent papers. The experimental results show that the proposed CTFWO algorithm has superior performance, and is competitive with many state-of-the-art algorithms outlined in some of the recent studies in terms of solution accuracy, convergence rate, and stability.

Optimal Energy Management for Multi-Microgrid Under a Transactive Energy Framework With Distributionally Robust Optimization

The increasing penetrations of renewable energy and electric vehicles bring more uncertainties and challenges to the existing power grid. The coordinated networked microgrids (MGs) contain renewable distributed generations (DGs) and nonrenewable DGs, which will be an important component in the future. We formulate an optimization problem based on a transactive energy (TE) framework for the energy schedule of upstream network and networked MGs to minimize the operation cost. The energy management between MGs and upstream network is operated by the distribution system operator (DSO), which is different from the direct control signal and fixed pricing mechanism in the traditional power system. We develop a distributionally robust optimization algorithm with ambiguity set based on Wasserstein distance (DROW) to solve the optimization problem with the uncertainties from real-time electricity price, renewable energy, loads, and electric vehicles. We carry out case studies about the energy schedule of the modified IEEE 33-bus and IEEE 118-bus power system with networked MGs. Numerical results indicate that the TE framework is conducive to schedule the energy of upstream network and networked MGs efficiently with the dynamic pricing scheme and the proposed DROW algorithm can seek a robust energy schedule of DSO and networked MGs with uncertainties.

Controlled Islanding Strategy Considering Uncertainty of Renewable Energy Sources Based on Chance-constrained Model

Controlled islanding plays an essential role in preventing the blackout of power systems. Although there are several studies on this topic in the past, no enough attention is paid to the uncertainty brought by renewable energy sources (RESs) that may cause unpredictable unbalanced power and the observability of power systems after islanding that is essential for back-up black-start measures. Therefore, a novel controlled islanding model based on mixed-integer second-order cone and chance-constrained programming (MISOCCP) is proposed to address these issues. First, the uncertainty of RESs is characterized by their possibility distribution models with chance constraints, and the requirements, e. g., system observ-ability, for rapid back-up black-start measures are also considered. Then, a law of large numbers (LLN) based method is employed for converting the chance constraints into deterministic ones and reformulating the non-convex model into convex one. Finally, case studies on the revised IEEE 39-bus and 118-bus power systems as well as the comparisons among different models are given to demonstrate the effectiveness of the proposed model. The results show that the proposed model can result in less unbalanced power and better observability after islanding compared with other models.

Novel Design of Slim Mould Optimizer for the Solution of Optimal Power Flow Problems Incorporating Intermittent Sources: A Case Study of Algerian Electricity Grid

Nowadays, electrical power grids are facing increased penetration of renewable energy sources (RES), which result in increasing level of randomness and uncertainties for its operational quality. In addition, emerging need for efficient solutions to stochastic optimal power flow (OPF) problem has attracted considerable attention to ensure optimal and reliable grid operations in the presence of generation uncertainty and increasing demand. Therefore, this paper proposes an efficient Slime Mould-inspired Algorithm (SMA) that aims to minimize overall operating cost of main grid by managing the power flow among different generating resources. The problem is formulated as large-scale constrained optimization problem with non-linear characteristics. Its degree of complexity increases with incorporation of intermittent energy sources, making it harder to be solved using conventional optimization techniques. However, could be efficiently resolved by nature-inspired optimization techniques without any modification or approximation into the original-formulation. The objective function is the overall cost of system, including reserve cost for over-estimation and penalty cost for under-estimation of both PV-solar and wind energy. The SMA performance is evaluated on the IEEE 30-bus test system and Algerian power system, DZA 114-bus. The SMA is compared with four optimization algorithms: i) The well-studied meta-heuristics, i.e., Gorilla troops optimizer (GTO), and Orca predation algorithm (OPA), ii) Recently developed meta-heuristics, i.e., Artificial ecosystem optimizer (AEO), Hunger games search (HGS), and Jellyfish search (JS) optimizer, iii) ad high-performance meta-heuristics, Success-History based parameter adaptation for differential evolution method. The overall simulation results reveal that the SMA ranked first among the compared algorithms, and so, over and so, over different function landscapes.

Wide-Area Network State Monitoring System Based On LoRa Communication

To meet the requirements of long range, small amount of data transmission, better data correctnessand less delay,a wide-Area Network State Monitoring System approach based on LoRa communication is discussed in our Project. The objective is to study and test the performance of Two communication techniques used in wide area monitoring system, a single input single output (SISO) system and a multiple input multiple output system (MIMO) in terms ofBit Error Rate(BER) with varying the signal to noise Ratio(SNR).In addition, we study the Wide Area Monitoring system based on LoRa communication. The IEEE 14-bus power system with Phasor Measurement Units(PMUs) placed at selected buses is considered. The simulation results demonstrate that the MIMO system outperform the SISO system in terms of Bit Error Rate. Furthermore, the value of the reporting rate has been varied for both systems in MATLAB to see the effect on the data correctness and the results show that the Bit Error Rate value decreases with the increase of reporting rate.Finally, we dealt with a modified LoRa MATLAB code emulator to investigate the data error and delay. The results show that LoRa Technology has better performance in terms of delay and error.

Optimal Management of Reactive Power Considering Voltage and Location of Control Devices Using Artificial Bee Algorithm

Reactive power compensation is one of the practical tools that can be used to improve power systems and reduce costs. These benefits are achieved when the compensators are installed in a suitable place with optimal capacity. This study solves the issues of optimal supply and the purchase of reactive power in the IEEE 30-bus power system, especially when considering voltage stability and reducing total generation and operational costs, including generation costs, reserves, and the installation of reactive power control devices. The modified version of the artificial bee colony (MABC) algorithm is proposed to solve optimization problems and its results are compared with the artificial bee colony (ABC) algorithm, the particle swarm optimization (PSO) algorithm and the genetic algorithm (GA). The simulation results showed that the minimum losses in the power system requires further costs for reactive power compensation. Also, optimization results proved that the proposed MABC algorithm has a lower active power loss, reactive power costs, a better voltage profile and greater stability than the other three algorithms.

Distributed filtering based on Cauchy-kernel-based maximum correntropy subject to randomly occurring cyber-attacks

This paper is concerned with the distributed filtering issue under the Cauchy-kernel-based maximum correntropy for large-scale systems subject to randomly occurring cyber-attacks in non-Gaussian environments. The considered cyber-attacks are hybrid and consist of both denial-of-service attacks and deception attacks. The weighted Cauchy kernel-based maximum correntropy criterion instead of the traditional minimum variance is put forward to evaluate the filtering performance against non-Gaussian noises as well as cyber-attacks. Based on the matrix decomposition and the fixed-point iterative update rules, the desired filter gain related with a set of Riccati-type equations is obtained to achieve the optimal filtering performance. Then, an improved version only dependent on the local information and neighboring one-step prediction is developed to realize the distributed implementation. Furthermore, the convergence of the developed fixed-point iterative algorithm is addressed via the famous Banach fixed-point theorem. Finally, a standard IEEE 39-bus power system is utilized to show the merit of the proposed distributed filtering algorithm in the presence of cyber-attacks and non-Gaussian noises.

A novel deep reinforcement learning enabled agent for pumped storage hydro‐wind‐solar systems voltage control

With the large-scale penetration of wind and solar energies in the power system, the randomness of this renewable energy increases the non-linear characteristics and uncertainty of the system, which causes a mismatch between renewable energy generation and load demand and it will badly affect the bus voltage control of distribution network. In this context, this study applies pumped storage hydroelectric (PSH) which tracks the load variation rapidly, operate flexibly and reliably to balance the power of the system to minimize the bus voltage deviation. Moreover, to obtain the optimal control policy of PSH, a deep-reinforcement-learning algorithm, that is, deep deterministic policy gradient, is utilized to train the agent to address the continuous transformation of the pumped storage hydro-wind-solar (PSHWS) system. The performance of a well-trained agent was evaluated on the IEEE 30-bus power system. Simulation results show that the proposed method achieves an improvement of 21.8% in cumulative deviation per month, which implies that it can keep the system operating in a safe voltage range more effectively.

Hybrid robust/stochastic transmission expansion planning considering uncertainties in generators’ offer prices: A second-order cone program approach

The aim of this paper is to propose a hybrid robust/stochastic model for transmission expansion planning (TEP) problem. The uncertainties related to demand and wind are modelled via stochastic programming (SP), while the uncertainties associated with the generators’ offer prices are modelled via robust optimization (RO). In this sense, an ellipsoidal uncertainty set is considered which the offer price values are known to lie in these given ellipsoids. The model presented is then divided into a master problem and several independent subproblems using a tailored implementation of Bender's decomposition (BD) method. Robust linear constraints of the lower level problems are then transformed into a convex second-order cone programming (SOCP) problem. We will apply the proposed model to IEEE-118 bus power system showing that generators’ offer price and their correlation can considerably affect the optimal plan of the transmission planning. It is observed that considering correlation among generators’ offer prices would not necessarily yield more expensive expansion plan in comparison with the case where correlation is ignored. Indeed, increase or decrease in investment cost depends on the entries of the variance-covariance matrix, network topology and the location of generators. Moreover, the results show the computational efficiency of the robust convex model.

Battery energy storage systems and demand response applied to power system frequency control

In this paper, several new control strategies for employing the battery energy storage systems (BESSs) and demand response (DR) in the load frequency control (LFC) task are proposed. In this way, first, the unit commitment problem considering the BESSs’ constraints in presence of wind farms and responsive loads is solved and the best location and the optimal size of the BESSs as well as the regulation power of the responsive loads are obtained. A rule-based plan is then suggested to improve the frequency regulation considering participation of wind farms. This plan is takes into account different states associated with power system frequency response as well as BESSs’ state of charge (SOC). The demand response (DR) program is another solution to enhance the frequency regulation which suffers from communication delay. To meet this issue, an adaptive latency compensator (ALC) is also designed here which consists of some fixed weighted compensators. The suggested ALC is a real time compensator in which the weights of fixed compensators are updated according to the amount of latency. To reach optimal response, the parameters of controllers in AGC and DR loops are tuned using the genetic algorithm (GA). To evaluate the proposed algorithms, some simulation studies are conducted in the IEEE 39-bus test power system. Results confirm the effectiveness of the proposed methods.

INCREASING AN ACCURACY OF CONTROL ACTION VOLUME CALCULATION IN CENTRALIZED EMERGENCY CONTROL SYSTEM FOR STATE ESTIMATION OF POWER SYSTEMS

The paper presents the results of the study on the effectiveness and advisability of voltage’s and current’s angles usage, collected from a wide-area monitoring system, to increase an accuracy of control actions volume calculation in case of power system’s state estimation. Centralized emergency control system architecture of a power pool system is shown to better understand the research core. We emphasize that the state estimation software module is the key module in a high level hardware and software package. Ways of telemetry and synchronized phasor measurements collection are outlined. For research practice, Gauss-Newton mathematical method is modified via measurement vector, vector-function, and scalar matrix of weight coefficients. Experiments are provided by IEEE 14-bus power system and 500–220 kV real backbone network. These power systems have several control areas, connected by interchanges. According to experiment results, we conclude that using not only voltage’s and current’s modules but also angles increases an accuracy of control actions volume calculation and effectiveness of a centralized emergency control system operation in the part of a control action formation. Therewith, the usage of current’s modules and angles raises the execution time of the state estimation software module. It is undesirable for real time systems operation. Therefore, it is reasonable to take into account current’s modules and angles only for those interchanges in emergency mode, when intensity factor, characterizing the limit of static stability, is more than 0.92. We also find out that control action volume calculation is sensitive to mistakes in current’s angles measurements. Thus, for reliable usage of current’s modules and angles as data for a state estimation and control action volume calculation, it is necessary to prevent timing errors of synchronized phasor measurement units and also develop a phase shift correction algorithm.

Sensitivity-based Optimal Remedial Actions to Damp Oscillatory Modes Considering Security Constraints

This paper proposes a comprehensive analytic method for applying various optimal remedial actions to improve critical electromechanical modes damping without jeopardizing damping of non-critical modes and violating security constraints of power system. Generators and reactive power sources redisptach, demand side management and the generators voltage reference tuning are remedial actions that are considered here. Dynamic equations of the flux-decay dynamic model of generators, standard dynamic models of excitation system and power system stabilizer and algebraic equations of active and reactive powers balance are formulated in the quadratic eigenvalue problem framework. With simultaneous use of system parameters and online measured data, sensitivity factors of electromechanical modes, generators reactive power, transmission lines apparent power and the buses voltage amplitude with respect to the mentioned remedial actions are calculated. Consumers, generators and reactive power sources are independent agents that offer their price bid to contribute in remedial action schemes. Finally, using sensitivity factors, the best remedial action is determined by mixed-integer nonlinear optimization subject to the minimum required damping ratio and security constraints of transmission lines congestion, generators reactive power capability and buses voltage amplitude. The proposed method is demonstrated on the IEEE 39-bus power system and the results highlight its efficacy.