Management Of Plug-in Electric Vehicles Research Articles

Distributed scheduling for multi-energy synergy system considering renewable energy generations and plug-in electric vehicles: A level-based coupled optimization method

Multi-energy synergy systems integrating high-penetration large-scale plug-in electric vehicles, distributed renewable energy generations, and battery energy storage systems have great potential to reduce the reliance of the grid on traditional fossil fuels. However, the random charging characteristics of plug-in electric vehicles and the uncertainty of photovoltaics may impose an additional burden on the grid and affect the supply–demand equilibrium. To address this issue, judicious scheduling optimization offers an effective solution. In this study, considering charge and discharge management of plug-in electric vehicles and intermittent photovoltaics, a novel Multi-energy synergy systems scheduling framework is developed for solving grid instability and unreliability issues. This formulates a large-scale mixed-integer problem, which calls for a powerful and effective optimizer. The new binary level-based learning optimization algorithm is proposed to address nonlinear large-scale high-coupling unit commitment problems. To investigate the feasibility of the proposed scheme, numerical experiments have been carried out considering multiple scales of unit numbers and various scenarios. Finally, the results confirm that the proposed scheduling framework is reasonable and effective in solving unit commitment problems, can achieve 3.3% cost reduction and demonstrates superior performance in handling large-scale energy optimization problems. The integration of plug-in electric vehicles, distributed renewable energy generations, and battery energy storage systems is verified to reduce the output power of 192.72 MW units during peak periods to improve grid stability. Therefore, optimizing energy utilization and distribution will become an indispensable part of future power systems.

Volt-Var Optimization With Power Management of Plug-In Electric Vehicles for Conservation Voltage Reduction in Distribution Systems

Volt-Var Optimization With Power Management of Plug-In Electric Vehicles for Conservation Voltage Reduction in Distribution Systems

A parallel meta-heuristic method for solving large scale unit commitment considering the integration of new energy sectors

In recent years, global warming impact are becoming increasingly severe due to the dramatic green house emission and severe environmental problem. The large integration of PEV and RGs directly affect the supply and demand balance of power grid, which bring challenges to the secure and economic operation of power system. This study proposes a novel parallel social learning particle swarm optimization method for solving the large scale power system scheduling problem with significant integration of RGs and PEVs. The novel algorithm combines the real value and binary decision variables obtained by social learning particle swarm optimization algorithm, aiming to solve large scale mixed integer unit commitment problem considering charging and discharging management of PEV with large RGs integration. To verify the effectiveness of the proposed algorithm, numerical examples are analyzed for multi scale unit numbers and various cases of RGs and PEVs. The results show that the proposed parallel social learning particle swarm optimization method has superior performance in solving UC problems considering new energy sectors. In addition, the case studies shows that the integration of new energy sources and flexible demand side management of plug-in electric vehicles have great potentials to alleviate power grid load and bring considerable economic benefits.

Charging management of plug‐in electric vehicles in San Francisco applying Monte Carlo Markov chain and stochastic model predictive control and considering renewables and drag force

The charging management of plug-in electric vehicles (PEVs) in San Francisco considering the effect of drag force on the vehicles, the real driving routes of vehicles, the social aspects of drivers' behaviour, the type of PEVs and the PEV penetration level is presented in this study. In this study, the drivers' responsiveness probability, to provide vehicle-to-grid service at the parking lot, is modelled with respect to the value of the incentive, drivers' social class and the real driving routes in San Francisco. Herein, the Monte Carlo Markov Chain is applied to estimate the hourly probability distribution function of the state of charge (SOC) of the PEV fleet in the day. The main data set applied in this study includes the real longitude and latitude of driving routes of vehicles in San Francisco, recorded in every four-minute interval of the day. In this study, a stochastic model predictive control is applied in the optimisation problem to address the variability and uncertainty issues of PEVs' SOC and renewables' power. Herein, quantum-inspired simulated annealing algorithm is applied as the optimisation technique. It is demonstrated that the type of PEVs, the PEV penetration level and even the social class of drivers can affect the problem results.

Demand side management of plug-in electric vehicles and coordinated unit commitment: A novel parallel competitive swarm optimization method

Decreasing initial costs, the increased availability of charging infrastructure and favorable policy measures have resulted in the recent surge in plug-in electric vehicle (PEV) ownerships. PEV adoption increases electricity consumption from the grid that could either exacerbate electricity supply shortages or smooth demand curves. The optimal coordination and commitment of power generation units while ensuring wider access of PEVs to the grid are, therefore, important to reduce the cost and environmental pollution from thermal power generation systems, and to transition to a smarter grid. However, flexible demand side management (DSM) considering the stochastic charging behavior of PEVs adds new challenges to the complex power system optimization, and makes existing mathematical approaches ineffective. In this research, a novel parallel competitive swarm optimization algorithm is developed for solving large-scale unit commitment (UC) problems with mixed-integer variables and multiple constraints – typically found in PEV integrated grids. The parallel optimization framework combines binary and real-valued competitive swarm optimizers for solving the UC problem and demand side management of PEVs simultaneously. Numerical case studies have been conducted with multiple scales of unit numbers and various demand side management strategies of plug-in electric vehicles. The results show superior performance of proposed parallel competitive swarm optimization based method in successfully solving the proposed complex optimization problem. The flexible demand side management strategies of plug-in electric vehicles have shown large potentials in bringing considerable economic benefit.

Charge management of plug-in electric vehicles for distribution transformer life enhancement

Charge management of plug-in electric vehicles for distribution transformer life enhancement

A Consensus-Based Cooperative Control of PEV Battery and PV Active Power Curtailment for Voltage Regulation in Distribution Networks

The rapid growth of rooftop photovoltaic (PV) arrays installed in residential houses leads to serious voltage quality problems in low voltage distribution networks (LVDNs). In this paper, a combined method using the battery energy management of plug-in electric vehicles (PEVs) and the active power curtailment of PV arrays is proposed to regulate voltage in LVDNs with high penetration level of PV resources. A distributed control strategy composed of two consensus algorithms is used to reach an effective utilization of limited storage capacity of PEV battery considering its power/capacity and state of charge. A consensus control algorithm is also developed to fairly share the required power curtailment among PVs during overvoltage periods. The main objective is to mitigate the voltage rise due to the reverse power flow and to compensate the voltage drop resulting from the peak load. Overall, the proposed algorithm contributes to a coordinated charging/discharging control of PEVs battery which provides a maximum utilization of available storage capacity throughout the network. In addition, the coordinated operation minimizes the required active power which is going to be curtailed from PV arrays. The effectiveness of the proposed control scheme is investigated on a typical three-phase four-wire LVDN in presence of PV resources and PEVs.

An Adaptive Approach for PEVs Charging Management and Reconfiguration of Electrical Distribution System Penetrated by Renewables

An adaptive approach for distribution system reconfiguration and charging management of plug-in electric vehicles (PEV) is presented in this study. A stochastic model predictive control is applied to stochastically, adaptively, and dynamically reconfigure the system, manage the incidental charging pattern of PEVs, and deal with the variable and uncertain power of renewable energy sources. The objective function of problem is minimizing daily operation cost of system. Herein, the geography of area is considered and the behavior of PEVs' drivers (based on their income level) is modeled with respect to the value of incentive and their hourly distance from each charging station. It is shown that behavioral model of drivers is able to affect the optimal results of problem. The simulation results demonstrate the competence of the proposed approach for cost reduction and making the problem outputs robust with respect to prediction errors.

A novel parallel-series hybrid meta-heuristic method for solving a hybrid unit commitment problem

Unit commitment is a traditional mixed-integer non-convex problem and remains a key optimisation task in power system scheduling. The high penetration of intermittent renewable generations such as wind and solar as well as mass roll-out of plug-in electric vehicles (PEVs) impose significant challenges to the traditional unit commitment problem, not only by significantly increasing the complexity of the problem in terms of the dimension and constraints, but also dramatically change the problem formulation. In this paper, a new hybrid unit commitment problem considering renewable generation scenarios and charging and discharging management of plug-in electric vehicles is first formulated. To effectively solve the problem, a novel parallel-series hybrid meta-heuristic optimisation method is then proposed, which combines a hybrid topology binary particle swarm optimisation, the self-adaptive differential evolution algorithm and a lambda iteration method, to simultaneously and intelligently determine the binary on/off status of each thermal unit, the generation power of online units, as well as the demand side management of plug-in electric vehicles. The proposed parallel-series hybrid method is first assessed on a 10-unit benchmark, and then on a case where renewable generation and smart PEV management are integrated. Numerical results confirm the superiority of the proposed new algorithm in comparison with some popular meta-heuristic approaches.

Escort Evolutionary Game Dynamics Approach for Integral Load Management of Electric Vehicle Fleets

This paper proposes an application of an evolutionary game dynamics called the escort dynamics (ED) for the decentralized load management of plug-in electric vehicles (PEV). Different from earlier contributions, in the present approach, PEVs work together in a fair scheme in order to provide several ancillary services to the grid: Load shifting, active power balancing, and partial supply of reactive power demand on each phase of the distribution transformer. Meanwhile, batteries are guaranteed to be fully charged according to the constraints imposed by the owners. In the proposed formulation, chargers can be either three phase or single phase; however, in this paper, only three-phase chargers are considered. The key concepts behind ED, especially for escort functions, are provided at the beginning of this paper. Based on these concepts, the assumptions and analogies followed for the construction of the proposed approach are explained in detail, especially for the proposed definition of escort functions. A multipopulation scenario is proposed for the interaction of several PEVs using local ED routines. This interaction among populations follows another well-known evolutionary game dynamics called the best reply dynamics. Performance is evaluated using real data measured from a distribution transformer from the SOREA utility grid company in the region of Savoie, France.

Active energy management strategies for active distribution system

Active energy management is an effective way to realize the flexible utilization of distributed energy resources to suit the characteristics of active distribution system. Advanced active energy management strategies need to be designed to coordinate the optimization of ‘generation, network, load’. An active management model is built for the local distribution system integrated with the generation curtailment mechanism and the charging/discharging management of plug-in electric vehicles. Furthermore, different strategies based on the energy management model are presented. The model and strategies are tested and discussed in a modified distribution system, and the impacts with different load profiles are also analyzed.

Optimal management of plug-in electric vehicles in smart distribution systems

This paper investigates optimal operation management of plug-in electric vehicles (PEVs) in distribution systems. In this regard, the high penetration of electric vehicles (EVs) especially in the form of PEVs is another source of energy that can help the grid if optimally managed. In order to make the existence of PEVs to a suitable opportunity for the grid, vehicle-to-grid (V2G) technology is employed to change the PEVs from moving loads into moving sources. We also suggest a novel smart strategy to first manage the operation of PV and PEVs for providing the operator targets and second model the uncertainties generated by both PV and PEV in the system. The proposed method uses particle swarm optimization algorithm (PSO) along with the point estimate method (PEM) to deal with these issues. The 69-bus IEEE test system is employed as the case study to examine the high performance and ability of the proposed algorithm.

Integrated Energy Management of Plug-in Electric Vehicles in Power Grid With Renewables

This paper presents an integrated control scheme for vehicle-to-grid (V2G) operation in the distribution grid with renewable energy sources. A hierarchical framework is proposed for V2G applications, and the mathematical models are built for both smart charging and V2G operation with distribution grid constraints. V2G power is regulated to minimize the total operating cost (TOC) while providing frequency regulation. The simulation results verify the control algorithm in coordinating distributed electric vehicle (EV) aggregations with the varying wind power and daily load. For V2G dynamic regulation, EVs connected in close proximity to wind power generators can locally compensate for the wind fluctuation with fast response and, hence, smooth out the power fluctuation at the bus having wind power generators and EVs. Each individual EV is strategically assigned to implement the simulated control algorithm through a bidirectional converter. An experimental platform is incorporated into the proposed integrated energy management to demonstrate the instantaneous response of EV battery storage.

Smart load management of plug-in electric vehicles in distribution and residential networks with charging stations for peak shaving and loss minimisation considering voltage regulation

New smart load management (SLM) approach for the coordination of multiple plug-in electric vehicle (PEV) chargers in distribution feeders is proposed. PEVs are growing in popularity as a low emission and efficient mode of transport against petroleum-based vehicles. PEV chargers represent sizeable and unpredictable loads, which can detrimentally impact the performance of distribution grids. Utilities are concerned about the potential overloads, stresses, voltage deviations and power losses that may occur in distribution systems from domestic PEV charging activity as well as from newly emerging charging stations. Therefore this study proposes a new SLM control strategy for coordinating PEV charging based on peak demand shaving, improving voltage profile and minimising power losses. Furthermore, the developed SLM approach takes into consideration the PEV owner preferred charging time zones based on a priority selection scheme. The impact of PEV charging stations and typical daily residential loading patterns are also considered. Simulation results are presented to demonstrate the significant performance improvement offered by SLM for a 1200 node test system topology consisting of several low-voltage residential networks populated with PEVs.