Distribution System Planning Research Articles

Explicit reliability incorporated switches planning of smart distribution system with high flexibility

Different types of switches play different roles in the fault management procedure in particular context of smart grid, which have different impact on system reliability. The motivation behind this paper is to develop an explicit reliability assessment method adapting to flexible deployment of circuit breakers (CBs), remote-controlled switches (RCSs), and manual switches (MSs), which can be further integrated into switch planning models analytically. The peculiar of this paper is to use the propagation constraints of fictitious power flows to model three stages of fault management procedures. Based on the fictitious power flows, the placement and action for CBs, RCSs, and MSs are modeled in detail as linear constraints, considering power flow constraints in fault management procedure. Therefore, this MILP model can be easily integrated into an exact mathematical optimization model. Finally, the proposed method is tested in 37, 85, 137, 417 node distribution network systems to verify the efficiency of the algorithm. Besides, the necessity of considering flexible location of three types of switches and its application on switch planning are verified.

Coordinated Planning of Electric Power and Natural Gas Distribution Systems With Refueling Stations for Alternative Fuel Vehicles in Transportation System

The alternative fuel vehicles (AFVs) which include electric vehicles (EVs) and natural gas vehicles (GVs) have gained much attention in recent years for delivering a clean and low-carbon urban mobility. The AFV development is promoting the coordination among electric power, natural gas, and transportation (ENT) networks. This paper proposes a coordinated planning method for investment on and operation of emerging urban energy infrastructures. The sites and sizes of refueling stations are optimized along with the expansion strategies for electric power distribution network (PDN) and natural gas distribution network (GDN). First, AFV refueling demands are analyzed according to the refueling station constraints and options, and transportation network (TN) constraints. Second, the coordinated planning model for PDN and GDN is formulated along with respective operation constraints in PDN, GDN, and TN. An efficient MILP solution method is developed for the proposed optimization problem. Finally, several case studies verify the effectiveness of the proposed model and its solution method. Compared with existing solutions, the proposed panning method offers a more accurate AFV refueling demand model with various refueling options. It is demonstrated that the coordinated planning can reduce investment and operation costs of urban energy infrastructures by exploring the pertinent interactions among constrained PDN, GDN, and TN.

Improvement of the Distribution Systems Resilience via Operational Resources and Demand Response

This article presents a restoration approach for improving the resilience of electric distribution systems (EDSs) by taking advantage of several operational resources. In the proposed approach, the restoration process combines dynamic network reconfiguration, islanding operation of dispatchable distributed generation units, and the prepositioning and displacement of mobile emergency generation (MEG) units. The benefit of exploring a demand response (DR) program to improve the recoverability of the system is also taken into account. The proposed approach aims to separate the in-service and out-of-service parts of the system while maintaining the radiality of the grid. To assist the distribution system planner, the problem is formulated as a stochastic-scenario-based mixed-integer linear programming model, where uncertainties associated with PV-based generation and demand are captured. The objective function of the problem minimizes the amount of energy load shedding after a fault event as well as PV-based generating curtailment. To validate the proposed approach, adapted 33-bus and 83-bus EDSs are analyzed under different test conditions. Numerical results demonstrate the benefits of coordinating the dynamic network reconfiguration, the prepositioning and displacement of MEG units, and a DR program to improve the restoration process.

Impact of situational awareness attributes for resilience assessment of active distribution networks using hybrid dynamic Bayesian multi criteria decision-making approach

The planning and operation of distribution systems have significantly changed due to the extensive osmosis of ICT (information and communication technologies) into power systems. Load growth, together with increased power quality expectations by the customers, has transformed the conventional distribution network to active distribution network (ADN), with penetration of distributed energy resources (DERs). This has imposed the necessity to analyze resilient performance of ADN subjected to the boon of ICT for enhanced situational awareness (SA). This paper focuses on a detailed multi-level hierarchy framework using linguistic scale based multi-criteria decision-making (MCDM) approach for SA oriented resilience level estimation of ADN, considering different high impact low frequency (HILF) events. The rapidly changing environmental conditions and ambiguity in interaction of network parameters lead to non-deterministic resilience (NDR), thus, affecting the operational resilience of ADN on a daily basis. To analyze the uncertain ADN availability due to NDR, hybrid MCDM-dynamic Bayesian network (DBN) approach is used. For better decision-making, the operational performance of ADN, to withstand critical loads due to NDR is analyzed in terms of load not served (LNS) index. The proposed framework is tested on IEEE 33-bus distribution network.

A hybrid knowledge-based method for pipe renewal planning in Water Distribution Systems with limited data: Application to Iran

This study uses a hybrid knowledge-based method for pipe renewal planning in Water Distribution Systems (WDSs), which is useful when data are limited. The method is applied to eight Iranian WDSs to demonstrate its effectiveness. An evolutionary systems design negotiation method was used to identify planning criteria for pipe renewal by a 17-member team of expert planners. A group of 48 experienced system operators then ranked the criteria by a nominal group technique. The results indicate when accurate operational data are limited, it is possible to use the combined expertise of knowledgeable planners and experienced operators for planning pipe renewal.

Investigation of Distribution System Stability with Incorporation of Capacitor Switching: A Case Study of Monatan 11 Kv Distribution System

Effectiveness of an electrical distribution system depends on the stability of its voltage profile. Several methods such as Static Compensator (SC), Thyristor Controlled Series Compensation (TCSC) and Series Capacitor have been applied for the stability of the system. However, these methods have been characterized by harmonic variation and low efficiency. Therefore, this research paper applied Shunt Switching Capacitor (SSC) on 11 kV distribution system located at Monatan, Ibadan, Oyo State in order to investigate the stability of the system during outages. Hourly data on bus voltage of the system were collected to determine the average bus voltage and voltage drop of the distribution system for stability evaluation using IEEE statutory limits method. Load Flow Model (LFM) of the distribution system was developed with incorporation of Capacitor Switching Compensation (CSC). This was solved using Kirchoff’s Current Law( KCL) analysis to improve the average bus voltage and voltage drop of the distribution system while simulation was done using MATLAB R2015a. The results of the average bus voltage and voltage drop of the distribution system were 10.5 and 4.2 , respectively. The CSC model improved the average bus voltage and voltage drop by 6 % and 33 % respectively. The research showed that incorporating capacitor switching as a compensation technique enhanced the stability of the distribution systems. The research is useful in the planning and optimization of electrical distribution systems. Keywords: Electrical Distribution System, Voltage, Capacitor Switching Compensation, Load Flow Models, Monatan 11 kV Distribution System, Voltage Drop, MATLAB. DOI: 10.7176/NCS/14-02 Publication date: August 31 st 2022

A Framework for Reliability Assessment in Expansion Planning of Power Distribution Systems

This article proposes a framework that uses analytical assessment of reliability to guide the expansion planning of power distribution systems (PDS) considering reliability criteria. The framework allows the estimation of reliability indices with and without the execution of expansion projects, thus supporting the decision-making process on investments in expansion projects. In the analytical assessment of reliability, failure rates of zones and restoration times are calculated from past data of interruptions in the primary distribution network. In addition, the estimated reliability indices are adjusted to historical values through failure rates proportionate to the length of each zone. To test and validate the proposed framework, it was applied to the distribution network at bus 5 of the Roy Billinton Test System (RBTS) and also to a real distribution feeder located in Brazil. The results indicated that the proposed framework can help define the most attractive investments leading to improvements in reliability indices and reduction in unsupplied energy. The estimation of reliability indices and energy not supplied, considered the following expansion alternatives: (i) the installation of normally-closed sectionalizing switches, (ii) the installation of normally-open switches with interconnection to adjacent feeders, (iii) the automation of switches, and (iv) the reconductoring of zones of the primary distribution network. Nevertheless, the proposed framework allows the inclusion of other expansion alternatives. Finally, the proposed framework proved to be handy and useful for real-life applications.

Integrated Planning of Cyber-Physical Active Distribution System Considering Multidimensional Uncertainties

The integration of cyber technologies is transforming the power distribution network into a cyber-physical active distribution system (CPADS), and brings more flexibility to its operation. However, various uncertainties from the cyber space and physical space are emerging and coupled with each other, which results in significant challenges for the distribution network planning and operation. Thus, a bilevel integrated planning model accounting for multidimensional uncertainties from both the cyber space and physical space is proposed for the CPADS in this paper. With the goal of minimizing the annual investment and operation costs, the model comprehensively considers the location and selection of active-management elements (e.g., electrical storage systems, capacitor banks, and switches), investment constraints, power flow constraints, and operation constraints for all the active-management elements, etc. Moreover, based on the typical cyber system failure scenario represented by information link failure (ILF), a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N-k</i> uncertainty set considering the failure probability distribution is proposed, while the uncertainties of renewable energy and load demand are characterized by box-like uncertainty sets. Finally, the proposed planning model is recast into a multi-stage robust optimization model which can be divided into a master problem and two-step subproblems (SP-1 and SP-2) to be solved by the column and constraint generation (CCG) algorithm. Case studies and comparative analysis on the PG 69 distribution system are performed to verify the effectiveness of the proposed method.

Optimal Planning of Secondary Power Distribution Systems Considering Renewable and Storage Sources: An Energy Management Approach

This study focuses on the optimal planning of secondary power distribution systems considering distributed renewable generators (DG) and energy storage systems (ESS) to minimize expansion costs. The methodology solves a mixed integer non-linear mathematical model that describes the planning problem, including the operating and technical aspects of the secondary power distribution system. Such methodology uses an iterated local search algorithm and a two-stage load flow decomposition method to solve said problem. The two-stage load flow decomposition method finds the optimal operation of the storage devices and the low-voltage distribution system for each solution proposed by the iterated local search algorithm; thus, optimal energy management is achieved for the best solution. The proposed methodology was tested on a real medium-sized secondary power distribution system to establish its effectiveness. The results obtained show a reduction of 51.97 % in the total energy purchase cost of the system and a decrease of 3.02 % in the installation costs of the secondary circuits and distribution transformers when DG and ESS are considered. In conclusion, the results show that the integration of these distributed energy resources into the distribution system planning problem increases the profits of distribution companies from energy purchase and sale and reduces their fixed costs.

Intelligent multiperiod wind power forecast model using statistical and machine learning model

With the rapidly increasing integration of wind energy into the modern energy grid system, wind energy prediction (WPP) is playing an important role in the planning and operation of an electrical distribution system. However, the time series data of wind energy always has nonlinear and non-stationary characteristics, which is still a great challenge to be accurately predicted. This paper proposes the intelligent wind power forecast model and evaluates to forecast long term, short term and medium term wind power. It uses statistical and machine learning approach for finding the best model for multiperiod forecasting. The model has been tested on Sotavento wind farm historical data, located in Galicia, Spain. The experimental results show that random forest has better accuracy than other models for long term, short term and medium term forecasting. The power prediction accuracy of the proposed model has been evaluated on RMSE, and MAE metrics. The proposed model has shown better accuracy for medium term and long term forecast. The accuracy is improved by 72.12% in case of medium term and 50.49% in case of long term.

Network performance evaluation of smart distribution systems using smart meters with TCP/IP communication protocol

The escalating significance of system reliability and resilience is transforming the planning and operation of contemporary distribution systems. To accomplish self-healing against power outages, advanced emerging technologies and devices such as smart meters are being deployed in the distribution systems. This work attempts to evaluate the performance of the smart distribution networks, where the communication protocol TCP/IP integrated with smart meters plays an essential responsibility. TCP/IP as a communication protocol has been used to send the data to the real world from simulation and receive it back into the simulation without losing data. Two-level control architecture has been proposed. The model has been designed using MATLAB/SIMULINK. To achieve it, the model consists of two LV distribution networks falling under one substation. Thus, two local controllers and one master controller have been modeled. Different single-phase loads (like domestic loads, colony loads having several houses, VIPs loads, shop loads, hospital loads, school loads, college loads, etc.) have been modeled as an amalgamation that has constant impedance and constant power loads. To simulate the functionalities of controllers, different case scenarios such as overcurrent protection, power theft, no shutdown of essential loads (like hospitals, VIPs), available power loss, and phase unbalancing in 42 residential loads altogether in two different areas have been studied and validated with real-time simulator through OPAL-RT.

Per capita water consumption for benchmarked South African service levels derived by means of explicit reasoning

Per capita water use is commonly employed in single-parameter models to estimate water demand, especially in regions where model input parameters are limited. Research has confirmed that the serviced population and household size positively correlate with water consumption, but the per capita consumption of household members decreases with increased household size. A central issue driving this study was the lack of an up-to-date per capita household water use guideline in the South African context. This study followed a process of explicit reasoning and inference, informed by an extensive knowledge review, stakeholder input and interrogation of relevant data, to develop a novel per capita water use estimation tool. Five main parameters were included, namely: (i) level of water service provided, (ii) usage scenario, (iii) household size (people per household), (iv) geographic region, and (v) regional property value. A Microsoft Excel–based tool was developed and is supplied online as supplementary material with this publication. The litres per capita per day tool (LCD-tool) allows for robust per capita water use estimates, as a function of the above five input parameters. The Microsoft Excel LCD-tool provides benchmarks for different South African conditions, described by context-specific service levels. The planning and management of water supply and distribution systems could benefit from the findings of this study.

Resilience-Oriented Framework for Microgrid Planning in Distribution Systems

Recently, it has been suggested that microgrids (MGs) can improve the resilience of distribution systems. However, predictions about future faults are uncertain. This makes calculating the exact value of the benefits of system resilience enhancement close to impossible at the time of MG planning. Therefore, this paper proposes a framework for MG planning, which focuses on resilience estimation. To consider the uncertainties of future failure events, the proposed method for estimating the resilience utilized the Monte Carlo simulation. In addition, an optimal scenario was estimated using a cost–benefit analysis and constraints on the expected value of resilience enhancement. In the case study, an actual MG installation at D-university was evaluated to obtain the optimal MG planning scenario. The results show that the capacity and installation locations of the distributed generators (DGs) impact the resilience enhancement. The proposed method can effectively derive the optimal MG planning scenario by evaluating the possibility of future operations based on the segmentation of both the system configuration and type of DG to improve the resilience of distribution systems.

Resiliency-oriented optimal siting and sizing of distributed energy resources in distribution systems

Distribution systems are one of the most important infrastructures of each country which are exposed to numerous damages through unexpected events. The development of distributed energy resources (DERs) is one of the solutions which can bring several benefits to the operation and planning of distribution systems in both normal and event situations. This paper proposes a new multi-objective planning model for optimal siting and sizing of DERs in distribution systems to minimize the total planning costs including operation and active power loss costs, as the normal operation objective, and to minimize the expected prioritized load shedding exposed to an earthquake incident, as the resilience improvement goal. Also, a novel incident-based outage scenario determination procedure is proposed using the fragility curves of distribution components to model the impact of earthquake events in resiliency evaluation stage. Since the proposed model is a non-linear and non-convex optimization problem with two contradictory objectives, the non-dominated sorting genetic algorithm II (NSGAII) followed by fuzzy decision making are utilized to find the Pareto solutions and the final optimal plan. Finally, the numerical results based on the IEEE 33-bus and IEEE 69-bus distribution systems are reported to evaluate the effectiveness of the proposed model.

Multistage Planning Model for Active Distribution Systems and Electric Vehicle Charging Stations Considering Voltage-Dependent Load Behavior

This work proposes a novel mixed-integer linear programming model for the medium-term multistage planning of active distribution systems and electric vehicle charging stations (EVCSs). Investment alternatives include the installation of conductors, capacitor banks, voltage regulators, dispatchable and nondispatchable distributed generation, energy storage units, and EVCSs. Hence, the model identifies the best size, location, and installation time for the candidate assets under the uncertainty associated with electricity demand, energy prices, renewable energy sources, and EVCSs’ load profiles. Unlike classical planning approaches, conventional load is modeled as voltage-dependent. Besides, EVCSs are planned by zones to optimize the coverage of the service provided to users of electric vehicles and to reduce the discrepancy between the geographical requirements and the optimal locations for the installation of EVCSs in the system. EVCSs’ load profiles are calculated using a travel simulation algorithm based on real travel patterns that consider fast, slow, and residential chargers. Moreover, as another salient feature, constraints for CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emissions are incorporated into the model. The resulting model is formulated as a stochastic scenario-based program, which is driven by the minimization of the total expected cost. Tests are conducted using a 69-node system to demonstrate the effectiveness of the proposed model.

Distribution system planning considering peak shaving of energy station

Energy stations (ESs) connected in a distribution system (DS) may lead to great impacts on the planning scheme of DS. In this context, this paper carries out a long-term DS planning model considering the peak shaving of ES, which is achieved by scheduling the input energy of ES. By regarding DS and ES as different stakeholders, a decentralized framework is devised to shave the electric peak loads in the DS planning, where the coupling relationship between the TOU price and exchanged power (e.g., the input power of ES) is clearly expressed. Specifically, an explicit adjustment formula is developed to represent the coupling relationship based on the concept of elasticity. In addition, an easily reformulated solution method is developed to address the probability distribution (PD) uncertainty of electric and cooling loads in the uncertainty-moment-based distributionally robust optimization (DRO) planning model. The chance-constrained power balance is expressed in a second order conic (SOC) format based on the conditional value at risk (CVaR) method and duality theory. Then, the SOC constraints are linearized according to the polyhedral linearization method. Furthermore, the bilinear terms of the planning model are respectively linearized by the McCormick and big-M methods. Finally, the proposed planning model is tested on a modified IEEE 33-node DS with an ES and a practical 99-node DS with an ES. Numerical results show that the proposed planning model is effective in managing PD uncertainties of loads as well as reducing costs of DS.

A comparative analysis for impact of distributed generations with electric vehicles planning

In the approaching years, the utilization of Distributed Generations (DGs) with Electric Vehicles (EVs) will accelerate. Future issues will be exacerbated by the relationship between the distribution system of DGs solely, DGs with Fuel Cell Electric Vehicle (FCEV), and DGs with Extended Plug-in Hybrid Electric Vehicles (Ex-PHEV). This paper discusses the use of a multi-tasking Genetic Algorithm to optimize DGs with EVs planning in distribution systems for load models for rating and placement determination. From a system perspective, the goal of this research is to reduce total real power loss. The various types of DGs with EVs are taken into account from the standpoint of the analysis. Photovoltaic, diesel engines, synchronous condensers, and doubly-fed induction generators are examples of various types of DGs. In this analysis, EV types such as FCEV and Ex-PHEV are considered. Different forms of constant impedance (Z), constant current (I), and constant power (P) load models are included in DGs with EVs planning for improving system performance indices, such as Light-emitting diode, Laptop charger, Personal computer, Tungsten light, and Vacuum tube. The real power loss index (%ILP), the reactive power loss index (%ILQ), the voltage deviation index (%IVD), the short circuit current reduction (%IC), the active power DGs with EVs penetration (% PPWDGs & EVs), and the reactive power DGs with EVs penetration (%PQWDGs & EVs) are all measured in this paper. On the 37-bus test distribution system, the effectiveness of the listed approach was verified.This article is very much useful for researchers, industrial, scientific persons, and those who are working in the smart grid with DGs and EVs.

The source-network coordinated planning of active distribution system based on linear constraints

To overcome the problems of poor convergence, high cost and long planning time of traditional methods, a source-network coordinated planning method of active distribution system based on linear constraints is proposed. Firstly, the objective function of source network collaborative planning of active distribution system is established, so as to build the source network collaborative planning model. Then, the system power balance, line power security and other constraints are analysed, and the line power constraints and node voltage constraints are transformed into linear constraints by sensitivity method. Finally, the artificial fish swarm algorithm is used to solve the model, and the output of the model is the source network cooperation planning result of the active distribution system. Experimental results show that this method achieves the convergence of the model solution algorithm in 30 times, the total planning cost is 3.512645 million yuan, and the planning time varies from 0.1 s to 0.5 s.

Bi-level Multi-objective Joint Planning of Distribution Networks Considering Uncertainties

With the increasing penetration of photovoltaics in distribution networks, the adaptability of distribution network under uncertainties needs to be considered in the planning of distribution systems. In this paper, the interval arithmetic and affine arithmetic are applied to deal with uncertainties, and an affine arithmetic based bi-level multi-objective joint planning model is built, which can obtain the planning schemes with low constraint-violation risk, high reliability and strong adaptability. On this basis, a bi-level multi-objective solution methodology using affine arithmetic based non-dominated sorting genetic algorithm II is proposed, and the planning schemes that simulta-neously meet economy and adaptability goals under uncertainties can be obtained. To further eliminate bad solutions and improve the solution qualities, an affine arithmetic based dominance relation weakening criterion and a deviation distance based modification method are proposed. A 24-bus test system and a 10 kV distribution system of China are used for case studies. Different uncertainty levels are compared, and a sensitivity analysis of key parameters is conducted to explore their impacts on the final planning schemes. The simulation results verify the advantages of the proposed affine arithmetic based planning method.

Critical Review of Data, Models and Performance Metrics for Wind and Solar Power Forecast

Global climatic changes and increased carbon footprints provided the main impetus for the decrease in the use of fossil fuels for electricity generation and transportation. Matured manufacturing technologies of solar PV panels and on-shore and off-shore windmills have brought down the cost of generation of electricity using solar energy on par with conventional fossil fuel. Initially, solar and wind power generation was envisioned for microgrids, serving small local communities. However, advancements in power electronics have now facilitated large solar and wind farms to be integrated with main power grids. In this context, hosting capacity, which is the amount of distributed energy resources a grid can accommodate, without significant infrastructure up-gradation, has gained importance. In determining the hosting capacity at a particular location, the uncertainties of wind and solar power generation play a role. Effective forecasting models using time-series weather data can be built to predict wind and solar power generation. This forecast is essential to ensure proper grid operation and control when renewable energy sources are already installed. The forecast is also useful in the planning stages for investment decisions and distribution system planning. While long-term forecasts are rarely needed for the operation of integrated grids, accurate short-term predictive models are necessary for scheduling. This paper presents an extensive review of various forecast models available in the literature. The study mainly focuses on the short-term forecast, providing a critical review of the duration of data used in each model and a synoptic comparison of their performance indices.