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

A Probabilistic Approach for the Optimal Sizing of Storage Devices to Increase the Penetration of Plug-in Electric Vehicles in Direct Current Networks

The growing diffusion of electric vehicles connected to distribution networks for charging purposes is an ongoing problem that utilities must deal with. Direct current networks and storage devices have emerged as a feasible means of satisfying the expected increases in the numbers of vehicles while preserving the effective operation of the network. In this paper, an innovative probabilistic methodology is proposed for the optimal sizing of electrical storage devices with the aim of maximizing the penetration of plug-in electric vehicles while preserving efficient and effective operation of the network. The proposed methodology is based on an analytical solution of the problem concerning the power losses minimization in distribution networks equipped with storage devices. The closed-form expression that was obtained is included in a Monte Carlo simulation procedure aimed at handling the uncertainties in loads and renewable generation units. The results of several numerical applications are reported and discussed to demonstrate the validity of the proposed solution. Also, different penetration levels of generation units were analyzed in order to focus on the importance of renewable generation

Towards Structuring Smart Grid: Energy Scheduling, Parking Lot Allocation, and Charging Management

Nowadays, the conventional power systems are being restructured and changed into smart grids to improve their reliability and efficiency, which brings about better social, economic, and environmental benefits. To build a smart grid, energy scheduling, energy management, parking lot allocation, and charging management of plug-in electric vehicles (PEVs) are important subjects that must be considered. Accordingly, in this dissertation, three problems in structuring a smart grid are investigated. The first problem investigates energy scheduling of smart homes (SHs) to minimize daily energy consumption cost. The challenges of the problem include modeling the technical and economic constraints of the sources and dealing with the variability and uncertainties concerned with the power of the photovoltaic (PV) panels that make the problem a mixed-integer nonlinear programming (MINLP), dynamic (time-varying), and stochastic optimization problem. In order to handle the variability and uncertainties of power of PV panels, we propose a multi-time scale stochastic model predictive control (MPC). We use multi-time scale approach in the stochastic MPC to simultaneously have vast vision for the optimization time horizon and precise resolution for the problem variables. In addition, a combination of genetic algorithm (GA) and linear programming (GA-LP) is applied as the optimization tool. Further, we propose cooperative distributed energy scheduling to enable SHs to share their energy resources in a distributed way. The simulation results demonstrate remarkable cost saving due to cooperation of SHs with one another and the effectiveness of multi-time scale MPC over single-time scale MPC. Compared to the previous studies, this work is the first study that proposes cooperative distributed energy scheduling for SHs and applies multi-time scale optimization. In the second problem, the price-based energy management of SHs for maximizing the daily profit of GENCO is investigated. The goal of GENCO is to design an optimal energy management scheme (optimal prices of electricity) that will maximize its daily profit based on the demand of active customers (SHs) that try to minimize their daily operation cost. In this study, a scenario-based stochastic approach is applied in the energy scheduling problem of each SH to address the variability and uncertainty issues of PV panels. Also, a combination of genetic algorithm (GA) and linear programming (GA-LP) is applied as the optimization tool for the energy scheduling problem of a SH. Moreover, Lambda-Iteration Economic Dispatch and GA approaches are applied to solve the generation scheduling and unit commitment (UC) problems of the GENCO, respectively. The numerical study shows the potential benefit of energy management for both GENCO and SH. Moreover, it is proven that the GENCO needs to implement the optimal scheme of energy management; otherwise, it will not be effective. Compared to the previous studies, the presented study in this paper is the first study that considers the interaction between a GENCO and SHs through the price-controlled energy management to maximize the daily profit of the GENCO and minimize the operation cost of each SH. In the third problem, traffic and grid-based parking lots allocation and charging management of PEVs is investigated from a DISCO’s and a GENCO’s viewpoints. Herein, the DISCO allocates the parking lots to each electrical feeder to minimize the overall cost of planning problem over the planning time horizon (30 years) and the GENCO manages the charging time of PEVs to maximize its daily profit by deferring the most expensive and pollutant generation units. In both planning and operation problems, the driving patterns of the PEVs’ drivers and their reaction respect to the value of incentive (discount on charging fee) and the average daily distance from the parking lot are modeled. The optimization problems of each DISCO and GENCO are solved applying quantum-inspired simulated annealing (SA) algorithm (QSA algorithm) and genetic algorithm (GA), respectively. We demonstrate that the behavioral model of drivers and their driving patterns can remarkably affect the outcomes of planning and operation problems. We show that optimal allocation of parking lots can minimize every DISCO’s planning cost and increase the GENCO’s daily profit. Compared to the previous works, the presented study in this paper is the first study that investigates the optimal parking lot placement problem (from every DISCO’s view point) and the problem of optimal charging management of PEVs (from a GENCO’s point of view) considering the characteristics of electrical distribution network, driving pattern of PEVs, and the behavior of drivers respect to value of introduced incentive and their daily distance from the suggested parking lots. In our future work, we will develop a more efficient smart grid. Specifically, we will investigate the effects of inaccessibility of SHs to the grid and disconnection of SHs in the first problem, model the reaction of other end users (in addition to SHs) based on the price elasticity of demand and their social welfare in the second problem, and propose methods for energy management of end users (in addition to charging management of PEVs) and model the load of end users in the third problem

Management and modelling of battery storage systems in microGrids and virtual power plants

In the novel smart grid configuration of power networks, Energy Storage Systems (ESSs) are emerging as one of the most effective and practical solutions to improve the stability, reliability and security of electricity power grids, especially in presence of high penetration of intermittent Renewable Energy Sources (RESs). This PhD dissertation proposes a number of approaches in order to deal with some typical issues of future active power systems, including optimal ESS sizing and modelling problems, power ows management strategies and minimisation of investment and operating costs. In particular, in the first part of the Thesis several algorithms and methodologies for the management of microgrids and Virtual Power Plants, integrating RES generators and battery ESSs, are proposed and analysed for four cases of study, aimed at highlighting the potentialities of integrating ESSs in different smart grid architectures. The management strategies here presented are specifically based on rule-based and optimal management approaches. The promising results obtained in the energy management of power systems have highlighted the importance of reliable component models in the implementation of the control strategies. In fact, the performance of the energy management approach is only as accurate as the data provided by models, batteries being the most challenging element in the presented cases of study. Therefore, in the second part of this Thesis, the issues in modelling battery technologies are addressed, particularly referring to Lithium-Iron Phosphate (LFP) and Sodium-Nickel Chloride (SNB) systems. In the first case, a simplified and unified model of lithium batteries is proposed for the accurate prediction of charging processes evolution in EV applications, based on the experimental tests on a 2.3 Ah LFP battery. Finally, a dynamic electrical modelling is presented for a high temperature Sodium-Nickel Chloride battery. The proposed modelling is developed from an extensive experimental testing and characterisation of a commercial 23.5 kWh SNB, and is validated using a measured current-voltage profile, triggering the whole battery operative range

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

Online Coordinated Charging of Plug-In Electric Vehicles in Smart Grid to Minimize Cost of Generating Energy and Improve Voltage Profile

This Ph.D. research highlights the negative impacts of random vehicle charging on power grid and proposes four practical PEV coordinated charging strategies that reduce network and generation costs by integrating renewable energy resources and real-time pricing while considering utility constraints and consumer concerns

Microgrid Energy Management

In IEEE Standards, a Microgrid is defined as a group of interconnected loads and distributed energy resources with clearly defined electrical boundaries, which acts as a single controllable entity with respect to the grid and can connect and disconnect from the grid to enable it to operate in both grid-connected or island modes. This Special Issue focuses on innovative strategies for the management of the Microgrids and, in response to the call for papers, six high-quality papers were accepted for publication. Consistent with the instructions in the call for papers and with the feedback received from the reviewers, four papers dealt with different types of supervisory energy management systems of Microgrids (i.e., adaptive neuro-fuzzy wavelet-based controls, cost-efficient power-sharing techniques, and two-level hierarchical energy management systems); the proposed energy management systems are of quite general purpose and aim to reduce energy usages and monetary costs. In the last two papers, the authors concentrate their research efforts on the management of specific cases, i.e., Microgrids with electric vehicle charging stations and for all-electric ships

IMPACT OF PLUG-IN ELECTRIC VEHICLES AND WIND GENERATORS ON HARMONIC DISTORTION OF ELECTRIC DISTRIBUTION SYSTEMS

Harmonic distortion on voltages and currents increases with the increased penetration of Plug-in Electric Vehicle (PEV) loads in distribution systems. Wind Generators (WGs), which are source of harmonic currents, have some common harmonic profiles with PEVs. Thus, WGs can be utilized in careful ways to subside the effect of PEVs on harmonic distortion. This work studies the impact of PEVs on harmonic distortions and integration of WGs to reduce it. A decoupled harmonic three-phase unbalanced distribution system model is developed in OpenDSS, where PEVs and WGs are represented by harmonic current loads and sources respectively. The developed model is first used to solve harmonic power flow on IEEE 34-bus distribution system with low, moderate, and high penetration of PEVs, and its impact on current/voltage Total Harmonic Distortions (THDs) is studied. This study shows that the voltage and current THDs could be increased upto 9.5% and 50% respectively, in case of distribution systems with high PEV penetration and these THD values are significantly larger than the limits prescribed by the IEEE standards. Next, carefully sized WGs are selected at different locations in the 34-bus distribution system to demonstrate reduction in the current/voltage THDs. In this work, a framework is also developed to find optimal size of WGs to reduce THDs below prescribed operational limits in distribution circuits with PEV loads. The optimization framework is implemented in MATLAB using Genetic Algorithm, which is interfaced with the harmonic power flow model developed in OpenDSS. The developed framework is used to find optimal size of WGs on the 34-bus distribution system with low, moderate, and high penetration of PEVs, with an objective to reduce voltage/current THD deviations throughout the distribution circuits. With the optimal size of WGs in distribution systems with PEV loads, the current and voltage THDs are reduced below 5% and 7% respectively, which are within the limits prescribed by IEEE