IEEE 33-bus Distribution System Research Articles

Application of Many-Objective Arithmetic Optimization Algorithm and TOPSIS for Optimal Planning of DGS in Distribution Systems

The traditional planning of distribution networks is changing because of the accelerated expansion of distributed generation (DG) technologies in various capacities and forms. However, the improper integration of DGs in current distribution networks can give rise to several technical difficulties despite the advantages provided by distributed generation technologies. This paper presents the optimal DG planning in the distribution system using a Pareto-based many-objective arithmetic optimization algorithm (MOAOA) for optimal DG planning problems in the distribution system. This work focuses on improving four technical metrics related to distribution systems: mitigation of electrical energy not served (EENS), total voltage deviation (TVD) minimization, voltage stability index (VSI) maximization, and energy loss mitigation. Two scenarios are considered: the first scenario primarily focuses on optimal planning of DGs supporting active power only (e.g. Micro-Turbines DGs), and the second scenario focuses on optimal planning of DGs supporting both active and reactive power support (e.g. BIOMASS DGs). The optimal Pareto fronts between the competing objectives are generated using the Pareto-based MOAOA algorithm. The TOPSIS (a technique for order performance by similarity to ideal solution) multi-criteria decision-making technique is utilized for selecting the best trade-off solution from the optimal Pareto front. The posited method is examined on two standard IEEE-69 bus distribution systems. The efficacy of the MOAOA is compared with the outcomes of MOPSO, MOGWO and NSGA-II.

Integrated Index Vector Method and Hybrid Ant Lion Chaotic Evolutionary Programming Optimization (HALCEP) Technique for Loss Minimization in Distribution System

Distribution system is the final section of the electrical power system, apart from the transmission and generation system. The distribution system acts as the interface that connects the high-voltage network to the low-voltage consumer service point. Consequently, the distribution system contributes the most of the overall losses in the electrical power system. Hence, evolutionary computation was introduced to accomplish a reduction of the previously said losses. The evolutionary computation involves optimization techniques to allocate distributed generation (DG) to the distribution system. Unfortunately, several optimization techniques are not capable of achieving accurate results and are trapped at local optima. This paper presents the Integrated Index Vector Method and Hybrid Ant Lion Chaotic Evolutionary Programming Optimization (HALCEP) technique for loss minimization in a distribution system. In this study, Evolutionary Programming (EP), Ant Lion Optimization (ALO) and HALCEP optimization engines are developed. The validation process was simulated on the IEEE 33-bus radial distribution system (RDS) model. Comparative studies were performed to gain a clear observation of the advantage of the developed HALCEP over the conventional EP and the ALO techniques, effectively showing its capability to outperform them.

A techno-economic scheduling of distribution system including multi-microgrids considering system reconfiguration and bus selector switches

The Microgrid (MG) as an alternative energy system, would reduce power loss, enhance energy consumption efficiency, and improve the system reliability for a sustainable city. Energy scheduling of distribution systems (DSs) including multi-microgrids (MMGs), would result in a multi-objective problem from DS operators’ point of view for a techno-economic operation. In this regard, a multi-objective model for operation scheduling of DS and connected MGs, is proposed in this paper, which also considers the system management solutions such as reconfiguration and determination of the energy trading points for MGs connection. The proposed model is applied to a modified IEEE 33-bus distribution system including 4 MGs. Simulation results and analysis based on the fuzzy satisfying method indicate that the simultaneous consideration of distribution system reconfiguration and Bus Selector Switches (BSSs) for MGs can improve the social welfare function by 4.58 % and reduces the energy losses by 15.82 %.

A novel sequence component based fault detection index for microgrid protection

The paper introduces a novel protective relaying method using symmetrical components of current to detect asymmetrical faults in microgrid. This approach is focused on utilizing the complex magnitude-phase plane (MPP) of the fault detection index (FDI), which is computed using the positive and negative sequence components of the current signal retrieved at one end of the faulty line. The MPP obtained from the settling values of the FDI's magnitude (MFDI) and FDI's angle (AFDI) after the fault inception, is considered as the key indicator for fault detection. The scheme is comprehensively evaluated for a wide range of fault situations including variations in fault types, fault locations, fault resistances and DG penetrations in grid-connected (GC) and non-grid-connected (NGC) modes of operation. Several critical no fault cases are also tested to show the efficacy of the proposed logic for false operation. The fault study is conducted extensively on a modified low voltage standard Consortium for Electric Reliability Technology Solutions (CERTS) microgrid and a standard IEEE-34 bus distribution system including DGs. Furthermore, the proposed FDI-based scheme is tested on MATLAB/Simulink platform and validated on Typhoon HIL platform to assess the real-time performance. The test results show that this FDI-based scheme using the MPP concept can be a potent solution for the protection of microgrid.

Optimal placement of distributed generation in power distribution system and evaluating the losses and voltage using machine learning algorithms

As the modern power system continues to grow in size, complexity, and uncertainty, traditional methods may occasionally prove insufficient in addressing the associated challenges. The improper location of distributed generation varies the voltage profile, increases losses and compromises network capacity. Machine learning algorithms predict accurate site positions, and network reconfiguration improves the capacity of the power system. The proposed algorithm is a hybrid of machine learning and deep learning algorithms. It cascades Support Vector Machine as the main model and uses Random Forest and Radial Neural Networks as classification algorithms for accurately predicting DG position. The non-linearity characteristics of the DG problem are directly mapped to the proposed algorithms. The proposed algorithm is employed on familiar test setups like the IEEE 33-bus and 69-bus distribution systems using MATLAB R2017 as simulation software. The R-squared (R2) values for all parameters yield a value of 1, while the MAPE values are minimal for the proposed cascaded algorithm in contrast to other algorithms of LSTM, CNN, RNN and DQL.

Optimizing Solar PV Placement for Enhanced Integration in Radial Distribution Networks using Deep Learning Techniques

This study introduces a highly effective technique to address the load flow challenge in Radial Distribution Networks (RDNs). The proposed approach leverages two matrices derived from the topological features of distribution networks to provide an optimal solution to handle load flow challenges. To assess the efficacy of this technique, simulations were executed on an IEEE 33-bus radial distribution system using MATLAB. Deep Learning (DL) has become a powerful artificial intelligence technique that excels at interpreting power grid datasets. Thus, a data-driven methodology is presented that incorporates an advanced Long-Short-Term-Memory (LSTM) network. Employing the Recurrent Neural Network with the LSTM (RNN-LSTM) technique based on these simulations, the study precisely identifies the optimal placement of an integrated PV generator within the radial network. The application of DL techniques, specifically LSTM networks, exemplifies the potential of data-driven approaches in enhancing decision-making processes. The results of this study highlight the potential of RNN-LSTM for the optimal integration of PV generators and for ameliorating the reliability of RDNs.

Network partition and distributed voltage coordination control strategy of active distribution network system considering photovoltaic uncertainty

Aiming at the voltage exceeding problem caused by the introduction of Photovoltaic (PV) in Active distribution network (ADN), a comprehensive electrical distance index was established considering active and reactive voltage sensitivity, and the uncertainty of traditional PV probability density function (PDF) was optimized by random simulation method. The electrical distance expectation matrix between nodes was established by discretization of PV output probability distribution characteristics, and the ADN system was partitioned by affinity propagation (AP) algorithm. Taking active power loss as the objective function, the non-convex reactive power optimization control problem is transformed into a convex quadratic programming problem by LinDistFlow equation. An improved alternating direction method of multipliers (ADMM) which adaptively adjusts the penalty parameters is used to convert the finite boundaries of adjacent regions into data. By coordinating on-load tap-changer (OLTC), capacitor banks (CBs), and PV inverters on different time scales, the fast optimal control of global voltage in ADN is realized. The proposed method is tested on IEEE 33-bus and IEEE 123-bus distribution systems. After the optimization control algorithm described in this paper, the system network loss was reduced by 3.88%. Compared with the semi-definite programming relaxation, the calculation speed of LinDistFlow equation is increased by 72.2%. The results fully verify the feasibility and high efficiency of the control strategy described in this paper.

Sustainability of Dynamic Wireless Power Transfer Roadway for In-Motion Electric Vehicle Charging

Numerous articles have been written regarding the potential affects that electric vehicle (EV) charging stations can have on power distribution systems. With the advent to adopt a dynamic wireless power transfer (DWPT) roadway charging infrastructure, there lies a potential for hazardous effects to occur. Therefore, this paper presents a novel methodology to analyze the sustainability and the potential effects that a DWPT roadway can have on electric power distribution grid. Historical traffic flow (HTF) data is converted using a Monte Carlo method to develop an EV traffic flow (EV-TF). Then, a full DWPT simulation of a 1-mile and 10-mile roadway is performed to determine the power fluctuations that can occur as different densities of EVs traverse the roadway. This method creates simulations using an IEEE 33-Bus power distribution system to evaluate if a DWPT roadway for in-motion EV charging causes voltage magnitudes to fluctuate beyond operational standards.

Optimal two-stage dispatch method of distribution network emergency resources under extreme weather disasters

In recent years, frequent extreme weather disasters have posed severe challenges to the safe and reliable power supply of distribution networks. In this paper, a resilience enhancement method is proposed for distribution networks, which establishes the full-phase analytical modeling of the distribution system defense and recovery process under extreme events, i.e., pre-event phase, degradation phase and restoration phase. Before a disaster strikes, multiple emergency resources are pre-allocated, including mobile emergency generators (MEGs) and repair crews and proactive islands are formed to improve the resistance of distribution networks to extreme weather disasters. After the disaster, real-time dispatching of MEGs and repair crews is coordinated with dynamic network reconfiguration, which considers the coupling processes among fault isolation, fault repair, and load restoration to improve the recovery ability of distribution networks. The IEEE 123-bus distribution system is used to verify the effectiveness and efficiency of the proposed method in enhancing the survivability of loads and the rapid recovery ability of distribution networks.

Dual Timescales Voltages Regulation in Distribution Systems Using Data-Driven and Physics-Based Optimization

A large number of electric vehicles (EVs), distributed solar and/or wind turbine generators (WTGs) connected to distribution systems lead to frequent and sharp voltages fluctuations. The action rates of conventional adjustable devices and smart inverters are very different. In this context, a novel dual-timescale voltage control scheme is proposed by organically combining data-driven with physics-based optimization. On fast timescale, a quadratic programming (QP) for balanced and unbalanced distribution systems is developed based on branch flow equations. The optimal reactive power of renewable distributed generators (DGs) and static VAR compensators (SVCs) is configured on several minutes or seconds. Whereas, on slow timescale, a data-driven Markovian decision process (MDP) is developed, in which the charge/discharge power of energy storage systems (ESSs), statuses/ratios of switchable capacitors reactors (SCRs), and voltage regulators (VRs) are configured hourly to minimize long-term discounted squared voltages magnitudes deviations using an adapted deep deterministic policy gradient (DDPG) deep reinforcement learning (DRL) algorithm. The capabilities of the proposed method are validated with IEEE 33-bus balanced and 123-bus unbalanced distribution systems.

A distributionally robust energy management of microgrid problem with ambiguous chance constraints and its tractable approximation method

As the penetration of intermittent renewable energy increases in microgrid systems, flexible power generation resources cause the power imbalance problem. To improve the absorption of renewable energy and to deal with its uncertainty more effectively, this paper proposes a distributionally robust approximate model for microgrid with ambiguous chance constraints (DR-CCP). First, the distributionally robust model with ambiguous chance constraints is a semi-infinite chance constrained planning problem, which is computationally difficult and inefficient, so the use of Chernoff's inequality to derive a safe tractable approximation form for the ambiguous chance constraint on the basis of a probability distribution for ambiguous sets including bounded perturbations with mean zero, transforms it into a mixed integer linear programming problem that can be directly solved using CPLEX to solve it. Then, the optimal value of the proposed model is used to approximate equivalent conditional value at risk (CVaR), and the relationship with CVaR is established. The optimal solution of our model is employed to construct multiple sets of comparison models, enhancing the richness of numerical experiments. Finally, to validate the feasibility and effectiveness of our proposed model, a series of diverse tests are performed on the IEEE 33-bus power distribution system.

Auction-based peer-to-peer energy trading considering echelon utilization of retired electric vehicle second-life batteries

Lately, the growing emergence of electric vehicles (EVs) brings problems of retired batteries proliferation. Seeking effective application scenarios targeting echelon utilization of retired EV second-life batteries (EVSLBs) becomes important. This paper proposes an auction-based P2P energy trading approach in a residential community market that incorporates retired EVSLBs utilization. A unified P2P energy trading model is proposed to coordinate the economic layer and the physical layer. In the economic layer, the household energy management system (HEMS) of each agent is in charge of operating and market bidding through optimization models to maximize their net profit. Comprehensive operation models of fresh batteries and retired EVSLBs have been proposed and embedded into HEMS models. The battery life degradation issue is also considered by calculating the cycling cost. The P2P energy trading is conducted by a double-sided auction method with an average pricing market (APM) mechanism, in which the re-bidding process is allowed. In the physical layer, voltage issues and line congestion management are considered. There is a community manager in the P2P market to ensure the economic layer decisions do not violate the constraints in the physical layer. The proposed models are tested on the modified IEEE 33-bus distribution system and the comparison between new batteries and retired EVSLBs in the community market is discussed based on the simulation results. The echelon utilization effectiveness of EVSLBs in residential P2P trading has been verified as well.

A conditional value at risk based stochastic allocation of SOP in distribution networks

Soft open points (SOPs) have been noticed to enhance the operation efficiency of distribution networks due to their capability of exchanging power between feeders as well as reactive power compensation. The uncertainty of distributed renewable energy sources (DRESs) and load demands make its optimal allocation and active power flow settings difficult. Uncertainties should be modeled through appropriate techniques to give more reliable schemes for SOPs allocation and regulation. However, in the probabilistic environment, decreasing the risk of violations from objectives and constraints is also important to guarantee confident solutions.This paper finds the optimal location of an SOP and its power flow setpoints, considering the uncertainty of DRESs and load demands. The mentioned uncertainties are handled by the efficient K-medoids data clustering technique. In addition to considering the expected value of losses and voltage deviation as objective functions, the conditional value at risk (CVaR) of voltage deviation is considered, too. This ensures that in the final solution, the probability of voltage deviation in most worst scenarios will not be considerable. The proposed optimization model is implemented in a multi-objective optimization frame and solved by the multi-objective particle swarm optimization (MOPSO) algorithm and tested on the modified IEEE 33-bus test distribution system and the effect of considering the CVaR index on results is comprehensively discussed.

A Distributed-MPC Framework for Voltage Control Under Discrete Time-Wise Variable Generation/Load

This article presents a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">distributed</i> predictive design for real-time voltage control under changing load and generation profiles at discrete intervals. The existing control designs include <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">centralized</i> approach, providing an optimal solution but less scalable and susceptible to single-point failures/attacks, as well as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">decentralized or localized</i> approach, having increased scalability and attack resilience but lacking optimality. The proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">distributed</i> solution offers the attractive features of both approaches, where the neighboring nodes share their local information to attain an optimal solution while retaining scalability and resilience to single-point failures/attacks. We first introduce the centralized version of the voltage control problem assuming grid observability and then transfer it to the distributed versions based on both bus-wise and area-wise decompositions of the network. The distributed version is solved via alternating direction method of multipliers (ADMM) that, for bus-wise decomposition, needs a full set of local measurements, whereas only a partial set of local measurements (that guarantee area-wise grid observability for each area) is needed for area-wise decomposition, along with neighbor-to-neighbor communications. Additionally, leveraging the availability of measurement data, the framework includes a distributed method to estimate the admittance matrix <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbf {Y}$</tex-math></inline-formula> of the underlying network graph. The proposed framework is validated against IEEE-30, IEEE-57 bus transmission systems, and IEEE-123 bus distribution systems and can tolerate certain levels of generation/load prediction uncertainties, modeling errors, and communication failures; plus, its in-built redundancy supports attack detection.

A Robust Data-Driven Process Modeling Applied to Time-Series Stochastic Power Flow

In this paper, we propose a robust data-driven process model whose hyperparameters are robustly estimated using the Schweppe-type generalized maximum likelihood estimator. The proposed model is trained on recorded time-series data of voltage phasors and power injections to perform a time-series stochastic power flow calculation. Power system data are often corrupted with outliers caused by large errors, fault conditions, power outages, and extreme weather, to name a few. The proposed model downweights vertical outliers and bad leverage points in the measurements of the training dataset. The weights used to bound the influence of the outliers are calculated using projection statistics, which are a robust version of Mahalanobis distances of the time series data points. The proposed method is demonstrated on the IEEE 33-Bus power distribution system and a real-world unbalanced 240-bus power distribution system heavily integrated with renewable energy sources. Our simulation results show that the proposed robust model can handle up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$25\%$</tex-math></inline-formula> of outliers in the training data set.

A Two-Point Estimate Method for Optimal Dispatching of Large-Scale Electric vehicles into Distribution Networks under Uncertainty

Electric vehicle (EV) charging irregularities have a massive effect on the distribution system. In recent years, research has focused on how to address the issue of large-scale charging and discharging of EVs while ensuring the grid's security. Distribution network operators should choose places where EV charging is practicable in terms of environmental and financial benefits. To establish how to best charge and discharge EVs in the distribution network, first comprehend and then determine the optimal position of EV parking lots in the distribution network. In this article, to solve this optimization problem, first the mathematical model of the problem is built, and then - according to the nonlinear features of the proposed optimization model - the second-order cone method is used to find the optimal solution with high accuracy and speed. The objective functions can accomplish more realistic objectives like lowering losses and minimizing voltage deviation changes with the charging and discharging time control technique, in addition to optimizing the technical requirements of the distribution network. The two-point estimation method of Hang is used to describe the uncertainty in the loads and capacities of solar sources in the random state, and optimization is applied to the IEEE 33-bus distribution system. The obtained results reveal that the proposed method not only meets the charging and discharging requirements of large-scale EVs, but also ensures the stability of the power grid, demonstrating the effectiveness of the proposed method in the optimal planning of EVs.

Cloud-Edge Collaboration-Based Distribution Network Reconfiguration for Voltage Preventive Control

The distribution network reconfiguration can realize the voltage preventive control of the distribution network under alert state, which solves the voltage security issues and maintains the safe operation of distribution network. However, since the great scale and complexity of distribution network would make the traditional centralized distribution network reconfiguration method have a heavy computing burden, a distribution network reconfiguration method based on cloud-edge collaborative architecture for voltage preventive control is proposed, which can reduce the huge computational pressure caused by the excessive concentration of computing tasks. In order to formulate the optimal topology reconfiguration strategy according to the specifics of voltage alerts, a differential hybrid Petri-net model with event-triggered strategy is constructed based on the cloud-edge collaborative architecture to characterize the logical relations of the solution process of the reconfiguration strategy. Considering the short time scale characteristic of preventive control, corresponding to the solution process described by the constructed model, an evaluation network based on graph convolutional neural network is proposed for the reachability discrimination of solutions to significantly reduce the number of candidate solutions, as well as a decision network based on multilayer perceptron is proposed for the selection of the optimal solution among the reachable solutions. Numerical tests are conducted on the modified IEEE 33-bus and IEEE 118-bus distribution systems to validate the effectiveness of the proposed method in dealing with voltage alert problems.

A Fully-Decentralized Transactive Energy Management Under Distribution Network Constraints via Peer-to-Peer Energy Trading

Peer-to-peer (P2P) energy trading is seen as a promising method for distributed transactive energy (TE) management. Since distribution network is used for electricity transfer, physical constraints should be imposed during the P2P trading. In this paper, we propose a P2P-based distributed TE management approach considering distribution network constraints and product differentiation for P2P energy trading between different buses. A multi-period AC optimal power flow (OPF) model is used to monitor power flow in distribution system, and the exactness of second-order cone relaxation is recovered by the convex-concave procedure, making sure that P2P transactions do not violate network constraints. Moreover, we propose a fully-decentralized variant of alternating direction method of multipliers for clearing P2P transactions in a fully-decentralized manner under AC power flow constraints. Our proposed variant does not need any interactions with the central supervisor in comparison with the traditional one. Finally, numerical results on IEEE 33-bus and 141-bus distribution systems verify the effectiveness of our proposed method in ensuring physical feasibility, lowering data communication and protecting data privacy during P2P energy trading.

Enhancing the performance of radial distribution systems via optimal integration of electric vehicles

&lt;span lang="EN-US"&gt;Electric vehicles (EVs) and their associated charging stations (CSs) are a promising avenue for greenhouse gas reduction and energy security. However, improper location and size of the EV charging station (CSs/EVs) negatively affect the power system. It results in some technical challenges such as increased real power-loss, decreased voltage stability and increased voltage deviation. This paper suggests optimum planning of CSs/EVs locations to conserve voltage, improve power-loss and reduce the effect of CSs/&lt;/span&gt;&lt;span lang="EN-US"&gt;EVs in electrical distribution systems. The multi objective genetic algorithm method (MOGA) is utilized to solve the optimization problem and reduce the impact of the random location of CSs/EVs. &lt;span&gt;The simulations are&lt;/span&gt; performed on IEEE 33-bus and IEEE 69-bus radial distribution systems (RDS). In addition, the optimal EV allocation is carried out with two fixed levels of loading from 100% to 150% of the candidate EV load. A comparison between the proposed MOGA technique and other optimization techniques is carried out. The results demonstrated the capability of the proposed technique for optimal placement of CSs/EVs in RDS. &lt;/span&gt;&lt;span lang="EN-US"&gt;Moreover, it is providing an objective value to solve a complex multi-objective nonlinear optimization problem to reduce the total power-loss, improve the voltage profile and extend the voltage stability.&lt;/span&gt;

Elasticity modelling of price-based demand response programs considering customer’s different behavioural patterns

Mathematical modelling of demand response programs (DRPs) aids regulators and policymakers in analysing the advantages of price-elastic loads on distribution system and electricity market. This mathematical model is used to find updated demand after DR for calculation of different technical and economical indices. In accordance with the concept of price elasticity of demand, an economic model of price sensitive load is developed. Price elasticity model (PEM) is a very useful tool to assess customer participation in DRPs. In this work PEM has been attributed on the ground of price elasticity of demand where PEM has been modelled by means of analytical and stochastic elasticity approach. Analytical elasticity has been modelled on the basis of ideal (lossless) approach whereas Stochastic elasticity has been modelled using Ornstein–Uhlenbeck process based on load flexibility and results obtained from these approaches were compared to check the acceptability of DR programs. It is observed that the proposed APEM and SPEM approaches provides maximum peak curtailment of 15% and 10.2% respectively during peak hours. Similarly, it is also noticed that both approaches improve load factor by reducing its peak-to-valley span by 47.33% and 35.7% respectively. The proposed models are investigated on standard IEEE 33-bus distribution system and modified IEEE 33-bus distribution system and are compared with already existing DR models. Technical and economical comparison of proposed approaches on both distribution systems shows that proposed models are competent enough to model customer behaviour properly.