Residential Feeders Research Articles

Empowering E-mobility: Day-ahead dynamic time of use tariff for electric vehicle charging

The greenhouse gases and preservation of the environment have been the most prioritized concern in many countries and international organizations, leading to set aims and initiatives for lowering the dependency on fossil fuels and minimizing carbon emissions. As a part of many initiatives, great emphasis is given to promoting electric vehicles (EVs) over their traditional carbon fuel-based counterpart. To support large-scale EV adoption, a dynamic tariff scheme is required that addresses complex issues like user comfort, savings, and utility revenue. In this research work, a dynamic pricing scheme is modeled for the centralized EV charging stations (EVCS) on a residential feeder. This pricing scheme integrates the existing static time of use (ToU) pricing to an hourly dynamic ToU tariff that varies according to the demand level in the residential feeder. The proposed tariff plan provides a day-ahead dynamic structure with a probable price variation range to aid the EV owners and the day-ahead charging mechanism to schedule the EVs beforehand. The developed scheme calculates the returns and adjusts the tariff including the demand charge and the recovery component on a daily, monthly, and yearly basis for the unsatisfactory investment revenue. Also, user convenience is ensured by setting the minimum possible tariff as well as satisfying the minimum revenue target with the integration of particle swarm optimization (PSO) in designing the pricing structure. The simulation results demonstrate the effectiveness of the proposed dynamic pricing scheme maintaining a trade-off between the interests of all parties involved. Moreover, the simulation outcome of the test case indicates that EV owners can save more than 32 % on charging bills if they plan correctly. On the other hand, the utility can earn more than 2.4 times the existing rate during the peak time of the residential feeder.

Comparative analysis of the reliability assessment of commercial and residential feeders in the power distribution utility of Nigeria

Reliability studies serve as valuable tools for assessing and optimizing system performance. Utilities with higher reliability indices are more likely to achieve break-even points due to significantly reduced downtime. This paper explores a comparative assessment of two 11 kV feeders supplying electricity to residential and commercial customers, addressing concerns about distribution system reliability in Nigeria and its impact on the country's GDP. The study involves a comprehensive reliability analysis, utilizing a flowchart to outline procedural steps and employing the ETAP Software program for data analysis collected over a one-month period from a power utility company. The data encompass operational parameters such as day-hourly consumption, outage records, and network equipment data. Results indicate higher reliability indices in the commercial feeder compared to the residential feeder, with the Customer Average Interruption Duration Index (CAIDI) being lower in the commercial feeder. The research underscores the significance of reliability assessment in improving operational efficiency, facilitating maintenance planning, and enhancing customer satisfaction.

Scheduling the charging and discharging events of electric vehicles for quasi dynamic load flow calculations of a low-voltage distribution grid with regard to stochastic behavior and grid requirements

In this study, a novel bidirectional Vehicle-to-Grid (V2G) scheduling application is presented to predict Electric Vehicle (EV) charging/discharging based on the user’s stochastic behavior and wishes, as well as the grid operator’s demand. The proposed application takes into account factors such as the initial battery State of Charge (SOC), user’s arrival time, user’s desired minimum and maximum SOC, EV unplugged time, peak-hour period, and the option of applying the grid operator control over EV charging start time. The effectiveness of the proposed application is then evaluated using various case studies in a single residential feeder of the low-voltage (LV) distribution network example in DIgSILENT PowerFactory. The simulation results indicate that appropriate management of EV charging/discharging through V2G technology can mitigate their negative effects on the LV distribution network, resulting in improved load profiles, voltage profiles, distribution lines loading, and power losses.

Effects of Household Battery Systems on LV Residential Feeder Voltage Management

With the advancements of the battery energy storage systems (BESSs), reduction of their manufacturing costs and government subsidies, the BESS uptake is likely to increase rapidly in power distribution networks. This paper investigates the effects of residential BESSs on low-voltage (LV) networks using the actual household load profiles equipped with BESS and solar-photovoltaic (PV) systems. The electricity consumption data collected via smart meters (2200 households with PV/BESS, 1950 households with PV only and 1000 households without a PV or a BESS) at different solar-PV penetration levels and network types are used to simulate real network operating scenarios. A real LV distribution network in Australia is analysed in DIgSILENT PowerFactory under different scenarios, such as, customers with and without a solar-PV/BESS, with a solar-PV but without a BESS, and without a solar-PV, by using both the power flow and quasi-dynamic simulation studies under balanced and the unbalanced network loading conditions. According to the study, customers experience large voltage excursions from solar-PV power exports, which could be resolved by the household BESS, provided that the BESS charging is coordinated with the solar-PV production. Moreover, quasi-dynamic simulation shows that the BESS could reduce the violation of the over-voltage limit during the solar peak hours (midday) by lowering the worst-case feeder voltage by 3%. Finally, extreme-event (high solar PV generation scenario) simulation shows that the implementation of the BESS controller to facilitate charging BESS during afternoon solar-PV export may reduce the negative grid impact and will assist to avoid network upgrades.

Congestion mitigation in unbalanced residential networks with OPF-based demand management

This paper proposes a novel congestion mitigation strategy for low voltage residential feeders in which the rising power demand due to the electrification of the transport and heating systems leads to congestion problems. The strategy is based on requiring residential customers to limit their demand for a certain amount of time in exchange for economic benefits. The main novelty of the method consists of combining a thorough representation of the network physics with advanced constraints that ensure the comfort of residential users, in a scalable manner that suits real systems. The mitigation strategy is presented from a DSO perspective, and takes the form of contracts between users and system operator. The focus on user comfort aims to make the contracts appealing, encouraging users to voluntarily enroll in the proposed mitigation scheme. The presented solution is implemented as a mixed-integer multi-period optimal power flow problem which relies on a linearized three-phase power flow formulation. Calculations on 100 real-life distribution feeders are performed, to analyze the congestion-relieving potential of several possible system operator-user contracts. From a planning perspective, the results can help the system operator define contractual terms that make a specific congestion mitigation scheme effective and viable. From an operational perspective, the same calculations can be used to optimally schedule power reduction on a day-ahead basis.

Planning battery energy storage system in line with grid support parameters enables circular economy aligned ancillary services in low voltage networks

Due to fast response time and the ability to charge and discharge efficiently, the battery energy storage system (BESS) has become a promising option for ancillary services in low voltage networks. The BESS planning is more critical where only selected cost parameters were considered, and the sustainability aspects were ignored in most cases. In this paper, BESS planning is re-imagined from a circular economy perspective that primarily allows the optimal use of battery capacities without compromising the grid support. A novel method for optimal siting and sizing of commercially available residential BESS for ancillary support in low voltage residential feeders is proposed. This method incorporates the cost of BESS operation in the planning stages to provide an accurate estimate of BESS for grid support. The implications of introducing BESS operation cost at the planning stages are demonstrated by solving four different optimization problems with the costs defined as over-voltage, voltage fluctuations, BESS costs (investment, O&M costs), and multiple objective operations. To validate, the proposed method is applied to an IEEE European low voltage feeder and a modified version of this test feeder to an Australian scenario using particle swarm optimization (PSO) algorithm. It is found that the proposed method successfully reduces the BESS costs and provides an appropriate estimation of how the BESS installations can provide grid voltage support. Compared to the existing methodologies for mitigating over-voltage, the net required BESS capacity for the European and Australian feeders was reduced by 7.21 percent and 9.58 percent, respectively. The total BESS capacity in the European and Australian grids was decreased by 41.48 percent and 48.06 percent, simultaneously compared with the methodologies for reducing the voltage fluctuations. Overall, the optimal sizing and siting approach we proposed not only reduces the voltage fluctuations and required battery capacities but also promotes circular economy by lowering the resources in the BESS planing for ancillary services.

IoT-based Communication Techniques of PVs Random Settlement in Residential Feeder

Whenever the additional random 1-5 kW rating and location photovoltaics (PVs) are connected into the residential feeder, the voltage profiles of the feeder become unbalanced. Besides the various load at the customer side has already imbalanced the feeder, the installed single-phase rooftop PVs do the same thing. They caused the power quality issues, namely voltage magnitude and angle, frequency, active and reactive powers become expressively recognized. Since the ancient electric utility infrastructure from power plants to customer side is now disrupting by the added settlement of several utility devices, the power quality issues have raisin significantly. This paper aims to implement the concept of several internets of things (IoT) technologies into the electric and communication infrastructures as they are able to virtuously cycle, enable, amplify and reinforce the utility development. To improve the imbalanced voltage and other power quality issues, this concept applied to the existed voltage regulation technique (VRT). The results show that the concept of IoT-based communication is successfully implemented if both the devices and technologies properly selected and designed. Additionally, the classification of communication layers, then the coordination of sensors, actuators, and meters along the residential feeder need to be considered as well.

IoT-based Communication Techniques of PVs Random Settlement in Residential Feeder

Whenever the additional random 1-5 kW rating and location photovoltaics (PVs) are connected into the residential feeder, the voltage profiles of the feeder become unbalanced. Besides the various load at the customer side has already imbalanced the feeder, the installed single-phase rooftop PVs do the same thing. They caused the power quality issues, namely voltage magnitude and angle, frequency, active and reactive powers become expressively recognized. Since the ancient electric utility infrastructure from power plants to customer side is now disrupting by the added settlement of several utility devices, the power quality issues have raisin significantly. This paper aims to implement the concept of several internets of things (IoT) technologies into the electric and communication infrastructures as they are able to virtuously cycle, enable, amplify and reinforce the utility development. To improve the imbalanced voltage and other power quality issues, this concept applied to the existed voltage regulation technique (VRT). The results show that the concept of IoT-based communication is successfully implemented if both the devices and technologies properly selected and designed. Additionally, the classification of communication layers, then the coordination of sensors, actuators, and meters along the residential feeder need to be considered as well.

Flexibility provision of residential energy hubs with demand response applications

Smart grids help local distribution companies (LDCs) facilitate the communication between grid operators and residential prosumers. While the prosumers are striving to optimize their daily load profile by home energy management systems (HEMSs) deployment, the LDCs attempt to enhance the operation of the grid in an efficient way and decrease network losses. This paper attempts to develop a new bi-level probabilistic optimization framework wherein an HEMS optimizes its respective daily load profile, and determines its flexibility provision, which is communicated to an LDC. In the proposed framework, a decentralized approach is used to achieve the flexibility product, which is the modulation of energy, through the incentive-based demand response (DR) programs. Finally, the LDC applies the prosumer's flexibility' offers to optimize its operational performance. The two-point estimate method (2PEM) is employed to model the uncertainties. The applicability of the framework is demonstrated by applying it to a system with one residential feeder.

Optimal Siting of Distributed Generation Unit in Power Distribution System considering Voltage Profile and Power Losses

Many underdeveloped countries are facing acute shortage of electric power and short term measures are important to consider to address the problems of power outage, power plant failures, and disaster areas. Distributed generation (DG) is a promising approach for such cases as it allows quick on-site installation and generation of electric power. Injection of DG can improve the system voltage profile and also reduce the system's total power losses. However, the placement and sizing of the DG unit is an optimization problem in the radial distribution system. As a test case, this study examines voltage profile improvement and system power losses for an 11 KV residential feeder at the Abdul Rehman Baba grid station in Pakistan, which is modelled using the Electrical Transient Analyzer Program (ETAP). For various scenarios, several tests are conducted to assess the effects of DG on the distribution system. The results show that proper design considerations of size and location of a DG, to be inserted in to the system, lead to significant reduction in power losses and improvement in voltage profile and thus improvement in the overall efficiency of the power system. The projections of this work can be used to optimize the expansion of a power system and tackling different issues related to voltage profile in distribution sector worldwide.

A Coordinated Charging Scheduling of Electric Vehicles Considering Optimal Charging Time for Network Power Loss Minimization

Electric vehicles’ (EVs) technology is currently emerging as an alternative of traditional Internal Combustion Engine (ICE) vehicles. EVs have been treated as an efficient way for decreasing the production of harmful greenhouse gasses and saving the depleting natural oil reserve. The modern power system tends to be more sustainable with the support of electric vehicles (EVs). However, there have been serious concerns about the network’s safe and reliable operation due to the increasing penetration of EVs into the electric grid. Random or uncoordinated charging activities cause performance degradations and overloading of the network asset. This paper proposes an Optimal Charging Starting Time (OCST)-based coordinated charging algorithm for unplanned EVs’ arrival in a low voltage residential distribution network to minimize the network power losses. A time-of-use (ToU) tariff scheme is used to make the charging course more cost effective. The concept of OCST takes the departure time of EVs into account and schedules the overnight charging event in such a way that minimum network losses are obtained, and EV customers take more advantages of cost-effective tariff zones of ToU scheme. An optimal solution is obtained by employing Binary Evolutionary Programming (BEP). The proposed algorithm is tested on IEEE-31 bus distribution system connected to numerous low voltage residential feeders populated with different EVs’ penetration levels. The results obtained from the coordinated EV charging without OCST are compared with those employing the concept of OCST. The results verify that incorporation of OCST can significantly reduce network power losses, improve system voltage profile and can give more benefits to the EV customers by accommodating them into low-tariff zones.

Impacts of COVID-19 related stay-at-home restrictions on residential electricity use and implications for future grid stability

“Stay-at-home” orders and other health precautions enacted during the COVID-19 pandemic have led to substantial changes in residential electricity usage. We conduct a case study to analyze data from 390 apartments in New York City (NYC) to examine the impacts of two key drivers of residential electricity usage: COVID-19 case-loads and the outdoor temperature. We develop a series of regression models to predict two characteristics of residential electricity usage on weekdays: The average occupied apartment’s consumption (kWh) over a 9am-5pm window and the hourly peak demand (Watt) over a 12pm-5pm window. Via a Monte Carlo simulation, we forecast the two usage characteristics under a possible scenario in which stay-at-home orders in NYC, or a similar metropolitan region, coincide with warm summer weather. Under the scenario, the 9am-5pm residential electricity usage on weekdays is predicted to be 15% – 24% higher than under prior, pre-pandemic conditions. This could lead to substantially higher utility costs for residents. Additionally, we predict that the residential hourly peak demand between 12pm and 5pm on weekdays could be 35% – 53% higher than that under pre-pandemic conditions. We conclude that the projected increase in peak demand - which might arise if stay-at-home guidelines coincided with hot weather conditions - could pose grid management challenges, especially for residential feeders. We also note that, if there is a longer lasting shift towards work and study-from-home, utilities will have to rethink load profile considerations. The applications of our predictive models to managing future smart-grid technology are also highlighted.

Design of On Load Tap Changing Transformer (OLTC) to Increase Photovoltaic Penetration Level in Low Voltage Distribution Feeder

This paper investigates a method for regulating the voltage profile and reducing the voltage unbalance at low voltage (LV) residential feeders with single-phase rooftop photovoltaic (PV) installed randomly along with the feeders. The used algorithm considers the distribution transformers that have on-load tap changers (OLTC) and can automatically control the voltage to prevent voltage rise in the feeder. MATLAB-based simulation results demonstrate the effectiveness of the discussed approaches.

Future search algorithm for optimal integration of distributed generation and electric vehicle fleets in radial distribution networks considering techno-environmental aspects

In this paper, a new nature-inspire meta-heuristic algorithm called future search algorithm (FSA) is proposed for the first time to solve the simultaneous optimal allocation of distribution generation (DG) and electric vehicle (EV) fleets considering techno-environmental aspects in the operation and control of radial distribution networks (RDN). By imitating the human behavior in getting fruitful life, the FSA starts arbitrary search, discovers neighborhood best people in different nations and looks at worldwide best individuals to arrive at an ideal solution. A techno-environmental multi-objective function is formulated using real power loss, voltage stability index. The active and reactive power compensation limits and different operational constraints of RDN are considered while minimizing the proposed objective function. Post optimization, the impact of DGs on conventional energy sources is analyzed by evaluating their greenhouse gas emission. The effectiveness of the proposed methodology is presented using different case studies on Indian practical 106-bus agriculture feeder for DGs and 36-bus rural residential feeder for simultaneous allocation of DGs and EV fleets. Also, the superiority of FSA in terms of global optima, convergence characteristics is compared with various other recent heuristic algorithms.

Technical and Financial Impacts on Distribution Systems of Integrating Batteries Controlled by Uncoordinated Strategies

This study assesses the technical and financial impacts on voltage levels, peak demand, and technical losses of distribution systems due to the integration of battery energy storage systems (BESSs) associated with photovoltaic distributed generation (PVDG). It assumes that the BESS features (location and storage capacity) are chosen by consumers individually, aiming to directly benefit themselves according to self-consumption and price arbitrage uncoordinated control strategies. Computational simulations of several PVDG/BESS penetration levels in residential feeders of a Brazilian utility were performed via OpenDSS, employing real data of the load (consumption and load profiles) and photovoltaic generation (irradiance and temperature). We address the PVDG/BESS locations and the selection of solar irradiance, temperature, and load profile based on the Monte Carlo method. As the impacts on voltage levels, peak demand, and technical losses are expressed in different units, they were stochastically converted into monetary amounts to identify the control strategy that enhances the technical benefits in the distribution system. The results show that BESS installation with location, size, and control strategy defined by consumers creates technical benefits for the distribution system. The risk analysis allows us to conclude that price arbitrage should be prioritized in deciding incentives for penetration levels of up to 60%. Above these levels, self-consumption should be prioritized as an incentive. However, it is worth mentioning that the two strategies could be encouraged regardless of the penetration levels, as both create technical benefits for the distribution networks. The proposed method can support policy proposals to encourage BESS integration in distribution systems.

Impact analysis of DERs on bulk power system stability through the parameterization of aggregated DER_a model for real feeders

With an ever increasing percentage of distributed energy resources (DERs) connected behind the meter in the distribution system, it is becoming increasingly important to equip transmission planners with the visibility of DER dynamic performance in distribution system. Not having visibility of the disconnection of DERs with the occurrence of transmission events, could result in an erroneous view of the stability of bulk power system.In this paper, a parameterized aggregated model (DER_a) serves as a representation of the distribution-level dynamics of real residential feeders, which is used for analysis of bulk power system stability. The parameters are obtained by executing dynamic Monte Carlo simulations. Faults are then induced at the substation level causing the DERs to trip which subsequently enables the parameterization of the low and high voltage breakpoints (vl0, vl1, vh0, and vh1) of the DER_a model’s partial voltage trip block. These parameters are then utilized to study the effectiveness of the DER_a model to represent the behavior of the aggregated DERs’ response and their impact on the bulk power system. The case study shows the ability of the positive-sequence DER_a model to provide an accurate estimation of DERs that are susceptible to trip due to 3-ϕ and 1-ϕ faults of transmission.

An Optimal Allocation of Reactive Power Capable End-User Devices for Grid Support

The increasing penetration of photovoltaic (PV) systems in low-voltage residential feeders has elevated the need for grid support at the distribution level to prevent violations of local voltage constraints. In this article, a coordinated reactive power support (RPS) methodology is presented that utilizes the demand-side flexibilities of the end user to keep local voltage levels within allowed levels. A cloud-based architecture is implemented to optimally coordinate consumers' reactive power capable demand-side resources such as electric vehicles, solar PV systems, flexible home appliances, etc., considering their varying characteristics, ratings, and purposes. An optimization-based two-stage device scheduling and management model is presented for the cloud server that schedules consumers' devices in day ahead for cost minimization, and optimally allocates the required RPS in real time among the candidate devices based on priority. Two device prioritization strategies are proposed that consider the reliability of reactive power capable consumer devices and management complexity, thereby, allowing consumers to either enhance the candidate devices' lifetime or reduce the management complexity while participating in grid support. The proposed RPS methodology is validated using simulation studies, and an experimental setup is established to verify the viability of the proposed cloud-based coordination system for RPS. Case studies indicate that the proposed method can effectively prevent overvoltage situations by using coordinated RPS from consumers' devices while maximizing their reliability. Results also indicate that the proposed methodology is economically more viable than state-of-the-art voltage control strategies.

Use of fitted polynomials for the decentralised estimation of network variables in unbalanced radial LV feeders

The lack of comprehensive monitoring equipment in low voltage (LV) residential feeders, impedes a near-term deployment of centralized schemes for the integration of domestic-scale distributed generation (DG). In this context, this paper introduces a technique that generates a set of fitted polynomials, derived from offline simulations and regression analysis, that characterise the magnitude of representative network variables (i.e. key for network operation) as a direct analytical expression of the controllable local conditions of any DG unit (i.e. active and reactive power injections). Crucially, the coefficients of these polynomials can be estimated, autonomously at the location of each DG unit, without the need for remote monitoring (i.e. using only locally available measurements). During online implementation, the method consists only of direct calculations (i.e. non-iterative), facilitating real-time operation. The accuracy of the polynomials to estimate the magnitude of the network variables is assessed under multiple scenarios on a representative radial LV feeder. Furthermore, the robustness of the method is demonstrated under the presence of new generation and electric vehicles.

Optimal Placement of Electric Vehicle Charging Stations in the Active Distribution Network

Electrification of the transportation sector can play a vital role in reshaping smart cities. With an increasing number of electric vehicles (EVs) on the road, deployment of well-planned and efficient charging infrastructure is highly desirable. Unlike level 1 and level 2 charging stations, level 3 chargers are super-fast in charging EVs. However, their installation at every possible site is not techno-economically justifiable because level 3 chargers may cause violation of critical system parameters due to their high power consumption. In this paper, we demonstrate an optimized combination of all three types of EV chargers for efficiently managing the EV load while minimizing installation cost, losses, and distribution transformer loading. Effects of photovoltaic (PV) generation are also incorporated in the analysis. Due to the uncertain nature of vehicle users, EV load is modeled as a stochastic process. Particle swarm optimization (PSO) is used to solve the constrained nonlinear stochastic problem. MATLAB and OpenDSS are used to simulate the model. The proposed idea is validated on the real distribution system of the National University of Sciences and Technology (NUST) Pakistan. Results show that an optimized combination of chargers placed at judicious locations can greatly reduce cost from $3.55 million to $1.99 million, daily losses from 787kWh to 286kWh and distribution transformer congestion from 58% to 22% when compared to scenario of optimized placement of level 3 chargers for 20% penetration level in commercial feeders. In residential feeder, these statistics are improved from $2.52 to $0.81 million, from 2167kWh to 398kWh and from 106% to 14%, respectively. It is also realized that the integration of PV improves voltage profile and reduces the negative impact of EV load. Our optimization model can work for commercial areas such as offices, university campuses, and industries as well as residential colonies.

On the application of Home Energy Management Systems for power grid support

Home Energy Management Systems (HEMSs) are being implemented for residential energy management in various parts of the world. Conventionally, a HEMS is developed from the consumer's perspective, with the principal aim of cost-saving while maintaining optimal consumers' comfort. In recent years, various Demand Response programs are being incorporated into HEMSs to address the power grid constraints. In this paper, the functionality of grid support through the HEMSs is presented. The developed scheme utilizes an agent-based coordination mechanism in an active distribution network and manages the household appliances to comply with thermal and voltage constraints of the grid. The proposed mechanism is evaluated through simulation of a typical Dutch low-voltage (LV) residential feeder. A hardware prototype has also been developed and tested in the laboratory environment. The proposed methodologies show promising perspectives for local voltage-violation support and direct load control for congestion management of the grid.