Optimal Online Charging Coordination of Plug in Electric Vehicles in Unbalanced Grids for Ancillary Voltage Support

This PhD thesis will propose an optimal online charge control through genetic algorithm for G2V coordination of PEVs (OL-C-TP) in unbalanced systems. Moreover the algorithm will be extended to also include V2G coordination and offer ancillary voltage support (OL-CD-TPQ) by considering two different methods based on the utility time-of-day prices for exporting reactive power and droop controller for decentralized exporting of reactive power. Then the performance of OL-CD-TPQ by switching PEVs in three phase unbalanced networks is improved

Reverse Engineering of Short Circuit Analyses

The electrical distribution system has evolved with embedded computer systems that can better manage the electrical fault that occurred around the feeders. Such random events can affect the reliability indices of overall systems. Computerized management system for distribution operation has been improving with the advanced sensing technologies. The general research question is here to articulate is the responsiveness for utility crew to pinpoint the exact location of a fault based on the SCADA fault indicators from pole-mounted feeder remote terminal units (FRTUs). This has been a tricky question because it relies on the information received from the sensors that can conclude fault with logic\u27s of over currents. The merit of this work can benefit at large the grid reliability because of time-saving in searching the exact location of a fault. The main contribution of this thesis is to utilize the 3-phase unbalanced power flow method to incrementally search for narrowing the localization of electrical short circuits. This is known as the reversal of the typical short circuit approach where a location of the fault is presumed. The 3 topological configurations of simulation studied in this thesis exhibit the typical radial configuration of a distribution feeder have been researched based on unidirectional and bidirectional power flow simulation. The exact fault location is carried in two steps. Firstly, a bisection search algorithm has been employed. Secondly, an incremental adjustment to match the simulated currents of fault with the measurements is conducted. Finally, the sensitivity analysis of a search can be improved with the proposed algorithm that leads to matching of telemetered and calculated values. The analysis of exact fault location is carried in unidirectional and bidirectional flow of power. Distributed energy resources (DER) such as residential PV at a household level as well the wind energy changes affect the protective relaying within a feeder as well as the reconfigurability of the switching sequences. Furthermost, the bidirectionality of power flow in an unbalanced manner would also be a challenging issue to deal with the power quality in automation. Finally, the simulation results based on unidirectional and bidirectional power flow are extensively discussed along with the future scope

Real‐Time Reconfiguration of Distribution Network with Distributed Generation

This chapter shows a methodology to accomplish the real‐time reconfiguration of distribution networks considering distributed generation in normal operating conditions. The availability of the wind power generation, solar photovoltaic power generation, and hydroelectric power generation is considered in the reconfiguration procedure. The real‐time reconfiguration methodology is based on the branch‐exchange technique and assumes that only remote‐controlled switches are considered in the analysis. The multicriteria analysis, analytic hierarchy process (AHP) method, is used to determine the best switching sequence. The developed algorithms are integrated into a supervisory system, which allows real‐time communication with the network equipment. The methodology is verified in a real network of a power utility in Brazil with different typical daily demand curves and distributed generation scenarios

A review on economic and technical operation of active distribution systems

© 2019 Elsevier Ltd Along with the advent of restructuring in power systems, considerable integration of renewable energy resources has motivated the transition of traditional distribution networks (DNs) toward new active ones. In the meanwhile, rapid technology advances have provided great potentials for future bulk utilization of generation units as well as the energy storage (ES) systems in the distribution section. This paper aims to present a comprehensive review of recent advancements in the operation of active distribution systems (ADSs) from the viewpoint of operational time-hierarchy. To be more specific, this time-hierarchy consists of two stages, and at the first stage of this time-hierarchy, four major economic factors, by which the operation of traditional passive DNs is evolved to new active DNs, are described. Then the second stage of the time-hierarchy refers to technical management and power quality correction of ADSs in terms of static, dynamic and transient periods. In the end, some required modeling and control developments for the optimal operation of ADSs are discussed. As opposed to previous review papers, potential applications of devices in the ADS are investigated considering their operational time-intervals. Since some of the compensating devices, storage units and generating sources may have different applications regarding the time scale of their utilization, this paper considers real scenario system operations in which components of the network are firstly scheduled for the specified period ahead; then their deviations of operating status from reference points are modified during three time-intervals covering static, dynamic and transient periods

Active congestion quantification and reliability improvement considering aging failure in modern distribution networks

The enormous concerns of climate change and traditional resource crises lead to the increased use of distributed generations (DGs) and electric vehicles (EVs) in distribution networks. This leads to significant challenges in maintaining safe and reliable network operations due to the complexity and uncertainties in active distribution networks, e.g., congestion and reliability problems. Effective congestion management (CM) policies require appropriate indices to quantify the seriousness and customer contributions to congested areas. Developing an accurate model to identify the residual life of aged equipment is also essential in long-term CM procedures. The assessment of network reliability and equipment end-of-life failure also plays a critical role in network planning and regulation. The main contributions of this thesis include a) outlining the specific characteristics of congestion events and introducing the typical metrics to assess the effectiveness of CM approaches; b) proposing spatial, temporal and aggregate indices for rapidly recognizing the seriousness of congestion in terms of thermal and voltage violations, and proposing indices for quantifying the customer contributions to congested areas; c) proposing an improved method to estimate the end-of-life failure probabilities of transformers and cables lines taking real-time relative aging speed and loss-of-life into consideration; d) quantifying the impact of different levels of EV penetration on the network reliability considering end-of-life failure on equipment and post-fault network reconfiguration; and e) proposing an EV smart charging optimization model to improve network reliability and reduce the cost of customers and power utilities. Simulation results illustrate the feasibility of the proposed indices in rapidly recognizing the congestion level, geographic location, and customer contributions in balanced and unbalanced systems. Voltage congestion can be significantly relieved by network reconfiguration and the utilization of the proposed indices by utility operators in CM procedures is also explained. The numerical studies also verify that the improved Arrhenius-Weibull can better indicate the aging process and demonstrate the superior accuracy of the proposed method in identifying residual lives and end-of-life failure probabilities of transformers and conductors. The integration of EV has a great impact on equipment aging failure probability and loss-of-life, thus resulting in lower network reliability and higher cost for managing aging failure. Finally, the proposed piecewise linear optimization model of the EV smart charging framework can significantly improve network reliability by 90% and reduce the total cost by 83.8% for customers and power utilities

Impact on the Distribution System due to Plug-In Electric Vehicles and Changes in Electricity Usage

Replacing conventional vehicles by Plug-in Electric Vehicles (PEVs) would likely increase electricity demand and put higher stress on the electrical power system. This thesis presents an approach to evaluate the impact on electrical distribution systems (DSs) caused by charging PEVs and load management of heating loads. The approach considers both vehicle usage statistics and demographic data to estimate when PEVs could be charged in different parts of a DS.A case study was performed on a residential and a commercial part of the DS in Gothenburg. Three different control strategies for the charging were investigated, i.e. uncontrolled, loss-optimal and price-optimal strategies. The control strategies would have a significant effect on the timing of the charging, as well as the access of available infrastructure for charging.The results showed that if all vehicles were PEVs and charged uncontrolled, peak demand would increase by between 21 - 35% in the residential area and by between 1-3% in the commercial area. If customers were directly exposed to the spot price at the Nordic day-ahead market and would charge according to the price-optimal control strategy, peak power would increase by 78% for the residential area and 14% for the commercial area. If the charging were controlled according to the loss-optimal control strategy, the charging would be conducted during off-peak hours without increasing peak demand, even if all vehicles were PEVs.By controlling the heating loads in the residential area according to the price-optimal control strategy peak demand would increase by more than 80%, while peak demand would be reduced by almost 10% if the loss-optimal control strategy were applied

Optimal Distribution Reconfiguration and Demand Management within Practical Operational Constraints

This dissertation focuses on specific aspects of the technical design and operation of a `smart\u27 distribution system incorporating new technology in the design process. The main purpose of this dissertation is to propose new algorithms in order to achieve a more reliable and economic distribution system. First, a general approach based on Mixed Integer Programming (MIP) is proposed to formulate the reconfiguration problem for a radial/weakly meshed distribution network or restoration following a fault. Two objectives considered in this study are to minimize the active power loss, and to minimize the number of switching operations with respect to operational constraints, such as power balance, line ow limits, voltage limit, and radiality of the network. The latter is the most challenging issue in solving the problem by MIP. A novel approach based on Depth-First Search (DFS) algorithm is implemented to avoid cycles and loops in the system. Due to insufficient measurements and high penetration of controllable loads and renewable resources, reconfiguration with deterministic optimization may not lead to an optimal/feasible result. Therefore, two different methods are proposed to solve the reconfiguration problem in presence of load uncertainty. Second, a new pricing algorithm for residential load participation in demand response program is proposed. The objective is to reduce the cost to the utility company while mitigating the impact on customer satisfaction. This is an iterative approach in which residents and energy supplier exchange information on consumption and price. The prices as well as appliance schedule for the residential customers will be achieved at the point of convergence. As an important contribution of this work, distribution network constraints such as voltage limits, equipment capacity limits, and phase balance constraints are considered in the pricing algorithm. Similar to the locational marginal price (LMP) at the transmission level, different prices for distribution nodes will be obtained. Primary consideration in the proposed approach, and frequently ignored in the literature, is to avoid overly sophisticated decision-making at the customer level. Most customers will have limited capacity or need for elaborate scheduling where actual energy cost savings will be modest

Operation of Distribution Systems with PEVs and Smart Loads

With the evolving concept of smart grids, Local Distribution Companies (LDCs) are gradually integrating advanced technologies and intelligent infrastructures to maximize distribution system capability, modernize the grid, and lay the foundation for smart loads. With the development of smart grids, utilities and customers will be able to coordinately send, retrieve, visualize, process and/or control their energy needs for the benefit of both. This thesis first presents a novel smart distribution system operation framework for smart charging of plug-in electric vehicles (PEVs). Thus, a three-phase Distribution Optimal Power Flow (DOPF) model is proposed, which incorporates comprehensive models of underground cables, transformers, voltage dependent loads, taps and switch capacitors, and their respective limits, to determine optimal feeder voltage-control settings and PEV smart-charging schedules. Various objective functions from the perspective of the LDC and customers are considered, and controlled tap, switch capacitors, and charging schemes are determined for various scenarios to address the shortcomings of uncontrolled charging, using two realistic feeder models to test and validate the proposed approach. Probabilistic studies are carried out on these two feeders, based on Monte Carlo Simulations (MCS), to account for the uncertainty in customers' driving patterns reflected in the initial PEV state-of-charge (SOC) and charging starting time. The thesis also presents mathematical models for price-responsive and controllable loads to study for the first time the smart operation of unbalanced distribution systems with these types of smart loads, based on the previously proposed DOPF model. The price-responsive load models are represented using linear and exponential functions of the price, while a constant energy load model, controllable by the LDC, is proposed to model critical and deferrable loads. The effect of feeder peak demand constraints on the controllable loads is also examined, based on the results obtained for two realistic feeder models. The two feeders are also used to study, using MCS, the variability of elasticity parameters and their impact on the output of the DOPF. Finally, the thesis presents a load model of an EHMS residential micro-hub using neural networks (NN), based on measured and simulated data. The inputs of the NN are weather, Time-of-Use (TOU) tariffs, time, and a peak demand cap imposed by the LDC. Various NN structures are trained, tested, validated, and compared to obtain the best fit for the given data. The developed function can be readily applied to the proposed DOPF for real-time optimal operation and control of LDC distribution feeders in smart grids