IEEE 30-bus Test System Research Articles

A Static State Estimation Scheme in Microgrid Utilizing μPMU Measurements

The operation of power systems needs uninterrupted and precise wide-area real-time monitoring for secure and automated control of power networks, therefore, there is a requirement of robust state estimation (SE) by the smart control centre. An iterative free static state estimation (IFSSE) scheme is developed using the linear measurement model (LMM) in this paper for microgrids (MGs) integrating distributed generation energy resources (DGERs). The developed scheme of IFSSE utilizes M-estimator (ME) concept, which is combined with the linear static state estimation (SSE) approach in order to provide robust estimation. The developed IFSSE scheme is iteration-free due to the implementation LMM that is achieved from placing the microphasor measurement units (μPMUs) and separate bad data processing (BDP) is not required for this proposed scheme. The proposed scheme's performance has been verified by employing it on modified IEEE-14 bus test systems (BTS) integrating DGERs and modified IEEE 30 BTS integrating DGERs then comparison has been done by taking two cases, i.e., linear SSE without BDP and linear SSE with BDP.

Optimized protection coordination of microgrids considering power quality-based voltage indices incorporating optimal sizing and placement of fault current limiters

Several solutions, such as fault current limiters (FCLs), have been utilized to mitigate the challenges of smart grids and ADNs’ protection schemes. However, less attention has been paid to simultaneously optimizing the protection coordination and power quality (PQ) concerns, incorporating the FCLs. There is a research gap in the literature in developing a method for optimal placement and impedance value of FCLs to optimize the speed of the protection systems, voltage sag energy index, and voltage sag duration. This research aims to fill such a research gap by proposing a new multi-objective optimization problem based on the optimum placement and impedance value of FCL. The proposed method is examined by implementing the distribution network of the IEEE 30-bus test system. MATLAB and DIgSILENT are linked online to simulate the protection of the smart grid and PQ voltage-based analyses and find the best solutions by the genetic algorithm (GA). The comparative analyses infer that a 13.13% improvement in criteria, e.g., operating time of relays, voltage sag energy, and voltage sag duration, is achieved by optimal placement of FCLs, beside other decision variables.

State Estimation for Electric Power System with Load Uncertainty and False Data Using Cuckoo Search Algorithm

State estimate serves a crucial purpose in the control centre of a modern power system. Voltage phasor of buses in such configurations is referred to as state variables that should be determined during operation. A precise estimation is needed to define the optimal operation of all components. So many mathematical and heuristic techniques can be used to achieve the aforementioned objective. An enhanced power system state estimator built on the cuckoo search algorithm is described in this work. Several scenarios, including the influence of load uncertainty and the likelihood of false data injection as significant challenges in electrical energy networks, are proposed to analyses the operation of estimators. The ability to identify and correct false data is also assessed in this regard. Additionally, the performance of the presented estimator is compared to that of the weighted least squares and Cuckoo Search algorithm. The findings demonstrate that the Cuckoo search algorithm overcomes the primary shortcomings of the conventional approaches, including accuracy and complexity, and is also better able to identify and rectify incorrect data. On IEEE 14-bus and 30-bus test systems, simulations are run to show how well the method works.

Robust Expectation-Maximization-Based Secondary Voltage Control Scheme Considering Stochastic Measurement Error

In this letter, the expectation-maximization-based secondary voltage control scheme is proposed to achieve the robust secondary voltage control (SVC) in the presence of stochastic measurement error under wide-area measurement system (WAMS). The proposed control scheme is implemented iteratively in the two steps, expectation and maximization steps. Since stochastic measurement error, following arbitrary parametric or nonparametric probability distribution, can be considered in the proposed control scheme, the proposed work can provide a robust and generalized solution for SVC in the presence of stochastic measurement error. Case studies on IEEE 300-bus test system are presented to verify the accuracy of the proposed control scheme.

Optimal dynamic reactive power compensation in power systems: Case study of Ecuador-Perú interconnection

Dynamic reactive compensation using Flexible AC Transmission Systems (FACTS) in power systems allows for regulation of the voltage within limits defined by the grid operator. Optimization algorithms that solve the optimal power flow (OPF) problem can be used to determine the operating range of a FACTS device. In this paper, we compare, in terms of total solve time, the solutions of two optimization algorithms for dynamic reactive power compensation, using the Branch Flow Model (BFM) and the Bus Injection Model (BIM) to represent the power flow equations. The algorithms to solve both problems have been compared using four cases: the adapted IEEE 14-Bus System, New England IEEE 39-Bus System, IEEE 118-Bus Test System, and the authors' proposed 20-Bus SNI-500 kV Ecuadorian System. The results allow us to identify that the convergence of BFM is achieved in less solve-time concerning BIM. Furthermore, both algorithms were applied to the case study of the future Ecuador-Perú 500 kV Interconnection, obtaining that the operating range of the SVC to be installed on the Peruvian side should supply between 58 MVAr and 209 MVAr.

Incorporating DSO’s Situational Awareness in Resilience-Oriented Distribution System Planning

One of the key distinctions between extreme weather events and random events in distribution systems is the level of predictability. The distribution system operator (DSO) can anticipate the upcoming extreme weather event and its effects, which is impossible in random events. Thus, the DSO can prepare the system by taking appropriate actions to tackle the upcoming event. This characteristic provides the DSO with operational situational awareness (SA) against extreme weather events. Considering this DSO’s capability in evaluating the system resilience against extreme weather events results in a more realistic resilience estimate. This paper proposes a model for resilience-oriented distribution system planning (RODP) in which the value of the DSO’s SA is considered and modeled (namely, SA-RODP). The proposed two-stage stochastic model is formulated as a mixed-integer linear programming (MILP) problem. In the first stage, hardening sections and installing emergency distributed generations (DGs), battery energy storage systems (BESSs), and automatic switches are modeled. The second stage contains the DSO’s decisions corresponding to operation scenarios. These decisions are made in two successive operation periods, namely, the preventive operation period (before the event to prepare the system) and the emergency operation period (during and after the event to mitigate the effects of the event). The DSO’s decisions in the preventive operation period are made according to each scenario, reflecting the DSO’s SA. The objective function of SA-RODP comprises the investment costs of the first stage and the expected operation cost of the second stage. The model constraints include the investment constraints and power flow and operational requirements constraints in each scenario. The model’s effectiveness is validated by several numerical simulations on the IEEE 33-bus test system. SA-RODP model simultaneously reduces the required investment cost and boosts the system resilience compared to conventional RODP frameworks in which the DSO’s SA is not considered. Further, this model increases the DSO’s capability to confront extreme weather events.

Optimal Placement of Distributed Generation Units for Microgrid Planning in Distribution Networks

Due to increasing penetration of renewable distributed generation (DG), conventional distribution networks have been gradually transforming into their active form, where microgrids may serve as fundamental building blocks. As the primary step towards microgrid planning, optimal DGs placement and sizing can reduce the total energy losses by localizing power supply to loads. In this paper, a DG optimal placement method by minimizing the total energy losses is proposed, where the planning is formulated as a non-linear programming (NLP) problem. AC optimal power flow (OPF) is used to solve this planning problem by considering operational constraints and uncertainties in load and renewable power generation of the network. IEEE 33-bus test system and a real 404-bus distribution system operated by Saskatoon Light and Power in Saskatoon, Canada are used to validate the proposed method. The proposed method also shows superior performance compared to existing methods.

A logistic regression approach for improved safety of the under-frequency load shedding scheme owing to feeder machine inertia

Under Frequency Load Shedding (UFLS) is the last resource to maintain the power system frequency after contingences. Load shedding can be performed by conventional, adaptive, computational or wide area monitoring techniques. The most common is a conventional UFLS. Load shedding is accomplished when predetermined frequency thresholds are reached. Although the conventional methods are simple, easily implemented and dependable, they may operate improperly. In the Brazilian electric power system, several actions of the UFLS scheme were found to be improper owing to the dynamics of induction motors and distribution generation during the main source outage of distribution feeders. In this way, when the main source is out of service, machine inertia maintains the frequency for some electric cycles, leading the frequency relay to undue tripping. In this paper, the negative sequence voltage and the rate of change of frequency are handled by a logistic regression method to identify the events of improper action. The proposed method was evaluated through the modified IEEE 9-bus test system, and several cases have been tested. The proposed method reached a level of performance higher than 99% of correct operation, and it needs a small training set.

Uncertainty-Informed Operation Coordination in a Water-Energy Nexus

The widespread deployment of smart heterogeneous technologies and the growing complexity in our modern society calls for effective coordination of the interdependent lifeline networks. In particular, operation coordination of electric power and water infrastructures is urgently needed as the water system is one of the most energy-intensive networks, an interruption in which may quickly evolve into a dramatic societal concern. The closely-intertwined ecosystem of water and power infrastructures is commonly known as water-energy nexus. This paper develops a novel analytic for uncertainty-aware day-ahead operation optimization of the interconnected power and water systems (PaWS). Joint probabilistic constraint (JPC) programming is employed to capture the uncertainties in wind resources and water demand forecasts. The proposed integrated stochastic model is presented as a non-linear non-convex optimization problem, where the non-linear hydraulic constraints in the water network are linearized using piece-wise linearization technique, and the non-convexity is efficiently tackled with a Boolean solution methodology to convert the proposed model with JPCs to a tractable mixed-integer linear programming (MILP) formulation that can be quickly solved to optimality. The suggested framework is applied to a 15-node commercial-scale water network jointly operated with a power transmission system using a modified IEEE 57-bus test system. The numerical results demonstrate the of the proposed stochastic framework, resulting in cost reduction (13% on average when compared to the traditional setting) and energy saving of the integrated model under different realizations of uncertain renewable energy sources (RESs) and water demand scenarios. Additionally, the scalability of the proposed model is tested on a modified IEEE 118-bus test system connected to five water networks.

Incorporating P2P Trading Into DSO's Decision-Making: A DSO-Prosumers Cooperated Scheduling Framework for Transactive Distribution System

The emerging prosumers have changed the way traditional distribution systems operate. While harvesting the benefits of Distributed Energy Resources (DERs), prosumers’ self-dispatching and internal energy trading can also bring technical challenges and economic loss to the distribution system operator (DSO). To this end, a DSO-prosumers cooperated scheduling framework for the transactive distribution system, which considers the impact of prosumers’ peer-to-peer (P2P) energy trading, is proposed in this paper. First, by incorporating the P2P energy trading among prosumers into scheduling, prosumers and the DSO can cooperate to optimize the power flow. Meanwhile, the Nash bargaining theory is introduced to achieve the optimal allocation of interests. Then, to improve the computational efficiency, the established bargaining problem is decomposed into two subproblems, i.e., the optimal physical power flow problem, and the virtual power flow bargaining problem. Finally, in order to protect individual privacy, each problem is solved via the alternating direction method of multipliers (ADMM) algorithm in a distributed manner. Case study based on the IEEE 33-bus test system verifies the effectiveness of the proposed method.

Forecasting Uncertainty Parameters of Virtual Power Plants Using Decision Tree Algorithm

Renewable energy sources (RESs) are increasingly used to meet the world’s growing electrical needs, especially for the economic benefits and environmental problems associated with fossil fuel use. Small-scale renewable energy sources, controllable loads, energy storage devices, and other nonrenewable sources are effectively integrated to form a virtual power plant (VPP). Uncertainty in forecasting renewable energy generation due to the intermittent nature of renewable energy sources is one of the biggest challenges in VPPs. Power generation by RESs changes with the day of the week, season, location, climate, and resource availability. Also, load demand and utility price vary with time and need to be forecasted for proper energy management of VPPs. However, the dispatching and planning of VPPs are significantly impacted by the volatile nature of RESs, load demand, and utility price. Predicting these uncertainties with high accuracy is essential to balance the electrical power generation and the load demand. In this article, a decision tree (DT) algorithm is proposed, to predict the uncertainty parameters, such as the day-ahead power from the RES, load demand, and utility prices of VPPs. The efficiency of the proposed model and the predicted results are compared with other complex models, such as the artificial neural network (ANN) and auto-regressive integrated moving average (ARIMA) algorithms. Root-mean square error (RMSE), mean square error (MSE), coefficient of determination (R 2), and mean absolute error (MAE) are the statistical metrics used to evaluate the accuracy of the prediction. One-year meteorological data of the Chennai zone in India is considered for predicting the uncertainty parameters. IEEE 16-bus and 33-bus test systems are used to validate the forecasting model. It is evident from the results that the proposed DT algorithm can predict the uncertainty parameters more accurately and use lesser time than the ANN and ARIMA algorithms.

Small signal stability constrained optimal power flow model based on trajectory optimization

The essence of traditional power system’s small signal stability analysis model using the eigenvalue analysis method is to analyze the time-invariant system obtained after the approximate linearization of the system, because it only considers the equilibrium state and therefore cannot consider the power system nonlinearity. In contrast, time-domain simulation can fully consider the system nonlinearity from the whole small signal dynamic period, because it considers not only the equilibrium state but also the state after the equilibrium point. On the basis of the time-domain simulation idea, this paper proposes an SSSC-OPF (small signal stability constrained-optimal power flow) based on optimizing the rotor angle trajectory. The objective function and the SSS constraint (small signal stability constraint) of the model are extracted from rotor angle curves of each generator, and the system stability is ensured by both in concert. Compared with the general SSSC-OPF model using the SSS constraint based on the eigenvalues, the proposed model can consider nonlinearity while avoiding the errors caused by the approximate linearization in the general SSSC-OPF model, and has a higher degree of generalizability. Finally, this paper performs the simulations in three test systems, the IEEE 9-bus test system, the IEEE 39-bus test system, and the IEEE 118-bus test system, and verifies the proposed model’s effectiveness by comparing the simulation results.

IMPACT AND EVALUATION OF OPTIMIZED PV GENERATION IN THE DISTRIBUTION SYSTEM WITH VARYING LOAD DEMANDS

Most distributed renewable energy generation (DREG) planning studies are performed using a constant load model and a dispatchable generation unit. However, the renewable generation unit and load demand vary in real life, and the generation size at the peak demand varies accordingly with loading levels. Such considerations may lead to the erroneous conclusion: the power loss reduction and bus voltage improvement may not be optimal. Consequently, the generation unit must be adequately integrated to offer optimal capacity in the distribution system while considering non-constant load demand as a part of DREG planning. Therefore, the impact of integrating photovoltaic (PV) considering historical solar weather data and varying load demand for five different voltage-dependent load models is proposed in this study. Particle swarm optimization (PSO) is employed to find the optimal location and size of PV with the objective to minimize power losses in the distribution system using IEEE 33-bus and IEEE 69-bus test systems. The findings are evaluated based on the comparative analysis of power losses reduction, PV penetration level, power loss index, and voltage deviation index. Findings revealed that the proposed model is effective in determining the optimal location and size of PV with a significant reduction of power losses that varies between 13.84% to 32.71% in 33-bus, and between 18.56% to 43.80% in 69-bus. In addition, the improvement in minimum bus voltage and other performance indices are also significant.

Application of Optimal Network Reconfiguration for Loss Minimization and Voltage Profile Enhancement of Distribution System Using Heap-Based Optimizer

A distribution system’s network reconfiguration (NR) is the process of changing the status of the switches to change the topology of the feeders. Using optimal NR at various network load levels, this research proposed an economical way for improving the voltage profile and reducing power loss in distribution systems. A new metaheuristic Heap-based optimizer based on the corporate rank hierarchy is used to solve the optimization problem in order to find the optimal radial distribution network. The objective functions used in this article include voltage profile improvement, power loss reduction, reduction of operation cost, and minimization of gas emissions. This novel technique provides accurate results while avoiding the issue of premature convergence and requires fewer control parameter settings. The proposed method is evaluated at three different load levels on a standard IEEE 33-bus test system, and the simulation results are compared to those of other optimization methods published in the literature, demonstrating that the proposed method improves the system voltage profile while minimizing losses and power costs, establishing that this technique is effective in finding the best result. The Wilcoxon and Friedman tests are also used to verify the effectiveness of the proposed technique.

Regression model-based adaptive receding horizon control of soft open points for loss minimization in distribution networks

This study proposes a method of using a soft open point (SOP) to flexibly connect different distribution networks. A novel operation strategy of SOP using a model predictive control (MPC) framework that adheres to the adaptive receding horizon control rule is developed to effectively respond to volatile renewable energy resources. Notably, the proposed method models the plant corresponding to voltage and network losses as a linear time-variant system, which provides better performance than the conventional MPC method in reducing network losses and improving voltage profile. To minimize the need for network observation of distribution system operators, we propose a process of deriving voltage-to-power and network loss-to-power sensitivity using a polynomial regression model. The effectiveness of the proposed strategy is verified on the modified IEEE 33-bus test systems, and its superiority is demonstrated through performance comparison with the existing general MPC method.

Solving optimal power flow problem for IEEE-30 bus system using a developed particle swarm optimization method: towards fuel cost minimization

ABSTRACT The optimal power flow (OPF) is an important technique for optimizing the control parameters of modern power systems by taking into account the desired objective functions considering system constraints. This paper provides a developed particle swarm optimization (PSO) method for solving the OPF problem with a rigorous objective function of minimizing generation fuel costs for the utility and industrial companies while satisfying a set of system limitations. By reviewing previous OPF investigations, the developed PSO is used in the IEEE 30-bus test system to reduce overall fuel cost, active/reactive power losses, and exhaust. The obtained results are compared to those obtained using a typical PSO technique and other algorithms. In the IEEE 30-bus test system, one of the paper’s key findings is that the cost of fuel is computed as 800.41 $/h, 830.7779 $/h, 825.6922 $/h, 826.54 $/h, 826.3176 $/h, 823.3999 $/h, 786.03 $/h with the conventional PSO, backtracking search algorithm (BSA), hybrid SFLA-SA, differential evolution (DE), enhanced GA (EGA), monarch butterfly optimization (MBO), proposed algorithm, respectively. Moreover, there is a great reduction in the fuel cost by 4.358% compared with the robust MBO algorithm. MATLAB software is used to demonstrate the effectiveness and accuracy of the proposed technique.

μPMU-based islanding detection method in power distribution systems

This paper focuses on the use of micro-phasor measurement units (μPMUs) for the prompt detection of unplanned islanding conditions. The investigated method is based on the monitoring of voltage and current phasors at distributed generation (DG) buses equipped by μPMUs. The difference between two consecutive phasors (voltage phasors and current phasors) are monitored until a perturbation occurs. When a perturbation is detected, the first phasor, called pre-disturbance phasor, is kept fixed and the second one, called post-disturbance phasor, is continuously updated with each new sample arrival. The differences between the pre-disturbance and post-disturbance phasors are calculated and summed up for a predefined time window. When this exceeds a predefined threshold, the island condition of the related bus is detected. The presented procedure can distinguish the islanding condition from other perturbations, such as motor starting, faults, and capacitor switching. The performance of the proposed procedure, as well as its sensitivity to the system conditions and predefined values, are analyzed considering a 7-bus distribution system and the IEEE 33-bus test system, both with synchronous generators and inverter-based DGs.

Distributionally robust optimal dispatching method of integrated electricity and heating system based on improved Wasserstein metric

This paper proposed a distributionally robust optimal dispatching (DROD) method of integrated electricity and heating system based on an improved Wasserstein metric, for dispatching problems to manage the risk from uncertain wind power forecasted errors and operating characteristics of electric vehicles (EVs). The ambiguity set employed in the distributionally robust formulation for wind power forecasted error is the Wasserstein ball centered at the empirical distribution and an adjustable uncertainty set of EV operating characteristics is proposed in this model to quantify the uncertainty of EVs more flexibly and intuitively. The proposed framework minimizes the generating cost, EV operating cost, and the expected generation units adjustable cost under the worst-case distribution in the uncertainty set. To improve the computational efficiency of the proposed DROD model, an improved Wasserstein metric based on extreme scenarios DROD (WMES-DROD) model is proposed to construct the ambiguity set of wind power forecasted error. The comparison between the Wasserstein metric-based DROD and WMES-DROD indicates that the proposed WMES-DROD model can obtain optimal results with less time. The out-of-sample analysis with the IEEE 118-bus test system confirms the excellent robustness and computational superiority of the proposed model.

Optimal Power Flow of Hybrid Wind/Solar/Thermal Energy Integrated Power Systems Considering Costs and Emissions via a Novel and Efficient Search Optimization Algorithm

The OPF problem has significant importance in a power system’s operation, planning, economic scheduling, and security. Today’s electricity grid is rapidly evolving, with increased penetration of renewable power sources (RPSs). Conventional optimal power flow (OPF) has non-linear constraints that make it a highly non-linear, non-convex optimization problem. This complex problem escalates further with the integration of renewable energy resource (RES), which are generally intermittent in nature. This study suggests a new and effective improved optimizer via a TFWO algorithm (turbulent flow of water-based optimization), namely the ITFWO algorithm, to solve non-linear and non-convex OPF problems in energy networks with integrated solar photovoltaic (PV) and wind turbine (WT) units (being environmentally friendly and clean in nature). OPF in the energy networks is an optimization problem proposed to discover the optimal settings of an energy network. The OPF modeling contains the forecasted electric energy of WT and PV by considering the voltage value at PV and WT buses as decision parameters. Forecasting the active energy of PV and WT units has been founded on the real-time measurements of solar irradiance and wind speed. Eight scenarios are analyzed on the IEEE 30-bus test system in order to determine a cost-effective schedule for thermal power plants with different objectives that reflect fuel cost minimization, voltage profile improvement, emission gases, power loss reduction, and fuel cost minimization with consideration of the valve point effect of generation units. In addition, a carbon tax is considered in the goal function in the examined cases in order to investigate its effect on generator scheduling. A comparison of the simulation results with other recently published algorithms for solving OPF problems is made to illustrate the effectiveness and validity of the proposed ITFWO algorithm. Simulation results show that the improved turbulent flow of water-based optimization algorithm provides an effective and robust high-quality solution of the various optimal power-flow problems. Moreover, results obtained using the proposed ITFWO algorithm are either better than, or comparable to, those obtained using other techniques reported in the literature. The utility of solar and wind energy in scheduling problems has been proposed in this work.

An Optimal Multi-Objective Approach for Distributed Generation and AVR Placement in Distribution Networks Using IPMBSA

Inclusion of automatic voltage regulators (AVRs) and distributed generation units (DGs) in the distribution system (DS) will enhance the system performance if allocated optimally. Optimal allocation of AVRs and DGs is a complex combinatorial problem requiring a powerful optimization algorithm for solving the optimization problem. This paper proposes an improved pareto multi-objective backtracking search algorithm (IPMBSA) based Pareto front approach for optimal siting and sizing of DGs and AVRs. The proposed approach considers DS technical (such as minimizing of voltage deviation, power losses and voltage stability index), cost and emission indices as objective functions while solving the allocation problem. To evaluate the performance of the proposed approach, five different operational scenarios are considered for analysis on IEEE 33-bus, IEEE 69-bus and practical 43-bus Indian distribution test systems. The effectiveness of the proposed approach is highlighted by comparison of results obtained using a multi-objective approach based on NSGA-II and weighted sum method (WSM). Results propounded that allocation of DG along with AVR yielded better DS performance compared to the scenarios where only DG or AVRs are placed in DS.