A Personalized Rolling Optimal Charging Schedule for Plug-In Hybrid Electric Vehicle Based on Statistical Energy Demand Analysis and Heuristic Algorithm

With the widespread use of electric vehicles (EVs), the potential to utilize them as flexible resources has increased. However, the existing vehicle-to-grid (V2G) studies have focused on V2G operation methods. The operational performance is limited by the amount of availability resources, which represents the flexibility. This study proposes a data-driven modeling method to estimate the V2G flexibility. A charging station is a control point connected to a power grid for V2G operation. Therefore, the charging stations’ statuses were analyzed by applying the basic queuing model with a dataset of 1008 chargers (785 AC chargers and 223 DC chargers) from 500 charging stations recorded in Korea. The basic queuing model obtained the long-term average status values of the stations over the entire time period. To estimate the V2G flexibility over time, a charging station status modeling method was proposed within a time interval. In the proposed method, the arrival rate and service time were modified according to the time interval, and the station status was expressed in a propagated form that considered the current and previous time slots. The simulation results showed that the proposed method effectively estimated the actual value within a 10% mean absolute percentage error. Moreover, the determination of V2G flexibility based on the charging station status is discussed herein. According to the results, the charging station status in the next time slot, as well as that in the current time slot, is affected by the V2G. Therefore, to estimate the V2G flexibility, the propagation effect must be considered.

This article studies an intelligent reflecting surface (IRS)-aided communication system under the time-varying channels and stochastic data arrivals. In this system, we jointly optimize the phase-shift coefficient and the transmit power in sequential time slots to maximize the long-term energy consumption for all mobile devices while ensuring queue stability. Due to the dynamic environment, it is challenging to ensure queue stability. In addition, making real-time decisions in each short time slot also needs to be considered. To this end, we propose a method (called LETO) that combines Lyapunov optimization with evolutionary transfer optimization (ETO) to solve the above optimization problem. LETO first adopts Lyapunov optimization to decouple the long-term stochastic optimization problem into deterministic optimization problems in sequential time slots. As a result, it can ensure queue stability since the deterministic optimization problem in each time slot does not involve future information. After that, LETO develops an evolutionary transfer method to solve the optimization problem in each time slot. Specifically, we first define a metric to identify the optimization problems in past time slots similar to that in the current time slot, and then transfer their optimal solutions to construct a high-quality initial population in the current time slot. Since ETO effectively accelerates the search, we can make real-time decisions in each short time slot. Experimental studies verify the effectiveness of LETO by comparison with other algorithms.

<div class="section abstract"><div class="htmlview paragraph">To provide useful information for automakers to design successful plug-in hybrid electric vehicle (PHEV) products and for energy and environmental analysts to understand the social impact of PHEVs, this paper addresses the question of how many of the U.S. consumers, if buying a PHEV, would prefer what electric ranges. The Market-oriented Optimal Range for PHEV (MOR-PHEV) model is developed to optimize the PHEV electric range for each of 36,664 sampled individuals representing U.S. new vehicle drivers. The optimization objective is the minimization of the sum of costs on battery, gasoline, electricity and refueling hassle. Assuming no battery subsidy, the empirical results suggest that: 1) the optimal PHEV electric range approximates two thirds of one's typical daily driving distance in the near term, defined as $450/kWh battery delivered price and $4/gallon gasoline price. 2) PHEVs are not ready to directly compete with HEVs at today's situation, defined by the $600/kWh battery delivered price and the $3-$4/gallon gasoline price, but can do so in the near term. 3) PHEV10s will be favored by the market over longer-range PHEVs in the near term, but longer-range PHEVs can dominate the PHEV market if gasoline prices reach as high as $5-$6 per gallon and/or battery delivered prices reach as low as $150-$300/kWh. 4) PHEVs can become much more attractive against HEVs in the near term if the electric range can be extended by only 10% with multiple charges per day, possible with improved charging infrastructure or adapted charging behaviors. 5) the impact of a $100/kWh decrease in battery delivered prices on the competiveness of PHEVs against HEVs can be offset by about $1.25/gallon decrease in gasoline prices, or about ¢7/kWh increase in electricity prices. This also means that the impact of a $1/gallon decrease in gasoline prices can be offset by about ¢5/kWh 23 decrease in electricity prices.</div></div>

This paper considers joint device activity detection and channel estimation in Internet of Things (IoT) networks, where a large number of IoT devices exist but merely a random subset of them become active for short-packet transmission at each time slot. In particular, to improve the detection performance, we propose to leverage the temporal correlation in user activity, i.e., a device active at the previous time slot is more likely to be still active at the current time slot. Despite the appealing temporal correlation feature, it is challenging to unveil the connection between the estimated activity pattern for the previous time slot (which may be imperfect) and the true activity pattern at the current time slot due to the unknown estimation error. In this paper, we manage to tackle this challenge under the framework of approximate message passing (AMP). Specifically, thanks to the state evolution, the correlation between the activity pattern estimated by AMP at the previous time slot and the real activity pattern at the previous and current time slot is quantified explicitly. Based on the well-defined temporal correlation, we further manage to embed this useful SI into the design of the minimum mean-squared error (MMSE) denoisers and log-likelihood ratio (LLR) test based activity detectors under the AMP framework. Theoretical comparison between the SI-aided AMP algorithm and its counterpart without utilizing temporal correlation is provided. Moreover, numerical results are given which show the significant gain in activity detection accuracy brought by the SI-aided algorithm.

With the widespread penetration of plug-in hybrid electric vehicles (PHEVs), the overall demand on microgrids (MGs) may increase manifold in the near future. Unregulated power demands from PHEVs may increase the demand–supply gap at MGs. Thus, to keep MGs stabilize and cater the ever-growing energy demands, there is a requirement of an intelligent solution to regulate and manage PHEVs in vehicle-to-grid (V2G) environment. Keeping in view the given issues, this paper proposes a novel scheme that aims to regulate PHEVs' charging and discharging activities based on MGs' day-ahead load curves. These load curves are obtained by utilizing the existing load forecasting techniques such as fuzzy logic (FL) and artificial neural networks (ANNs). Efficient utilization of PHEVs according to these curves may play a vital role in flattening MG's load profile. Thus, the proposed scheme works by reserving resources such as time slots and charging points (CPs) for PHEVs during peak shaving and valley filling. Different algorithms pertaining to resource reservation for PHEVs have also been designed. These algorithms employ the concepts of game theory and the 0/1 knapsack problem for supporting peak shaving and valley filling, respectively. Moreover, PHEVs are also utilized when there are transitions from valley filling to peak shaving areas in the load curves and vice versa . PHEVs involved in this process have both charging and discharging capabilities and are referred to as dual-mode PHEVs. The proposed scheme has been tested with respect to various parameters, and its performance was found satisfactory.

Enabling a bidirectional energy flow between power grids and plug-in hybrid electric vehicles (PHEVs) using vehicle-to-grid (V2G) and grid-to-vehicle (G2V) communications is considered as one of the key components of the future smart grid. On the one hand, the PHEV owner needs to charge its PHEV through the grid, given possibly time-varying electricity pricing schemes. On the other hand, the energy stored in a PHEV can also be sold back to the grid so as to act as an ancillary service while possibly generating revenues to its owner. Consequently, this motivates the need to develop smart charging policies that enable the PHEV owner to optimally decide on when to charge or discharge its vehicle, while minimizing its long-term energy consumption cost. In this paper, we model this PHEV energy management problem as a Markov decision process (MDP), which is solved by using a linear programming (LP) technique so as to obtain the optimal charging policy. In particular, we devise optimal charging policies that are resilient to the price information attacks such as denial of service (DoS) attacks and price manipulation attacks over the grid's communication network. We show that, under potential price information attacks, each PHEV can optimize its charging policies given only an estimated price information, which leads to a discrepancy between the real and expected costs. To this end, we analyze this cost difference using the proposed MDP model, which can also guide the system designer and administrator to decide whether reinforcing the system's security is required. The simulation results show that the proposed PHEV charging policy is effective and is adaptable to different PHEV mobility patterns, battery levels and varying electricity prices. It is also demonstrated that improving the system's ability to detect and resolve the attack can obviously reduce the impact brought by the attacks.

As the penetration level of plug-in hybrid electric vehicles (PHEVs) increases, the charging demand of PHEVs is expected to have a major impact on the loading of distribution systems. Charging access control, which determines the starting time of PHEV charging, is critical to mitigate such impact. In this paper, we investigate how to achieve PHEV charging access control by leveraging the electricity price set by electricity retailer and the speculative prices of PHEV owners. A firm-union bargaining game approach is proposed to study the interactions between the electricity retailer and PHEV owners. In order to eliminate the requirement of a centralized energy management system, we extend the original centralized firm-union bargaining game approach to a semi-distributed approach. In particular, the retailer plays the role of the union for wage control by setting the electricity price for PHEV charging, while balancing the revenue and cost. On the other hand, each PHEV owner uses a responsive strategy to optimize his/her own benefit by choosing an appropriate charging starting time. Through nonlinear programming, a sub-game perfect Nash equilibrium solution can be attained. The PHEV owners in the game can determine their strategies independently to maximize their own benefits. The model is further extended to consider the cost of charging demand fluctuation reflected by the cost of purchasing frequency regulation or spinning reserve services by distribution utilities. Extensive numerical results are presented to evaluate the performance of the proposed approach.

The potential influence of cost-related factors on the adoption of electric vehicle: An integrated micro-simulation approach

This paper investigates real-time self-dispatch of a remote wind-storage integrated power plant connecting to the main grid via a transmission line with a limited capacity. Because prediction is a complicated task and inevitably incurs errors, it is a better choice to make real-time decisions based on the information observed in the current time slot without predictions on the uncertain electricity price and wind generation in the future. To this end, the operation problem is formulated under the Lyapunov optimization framework to maximize the long-term time-average revenue of the wind-storage plant. Inter-temporal storage dynamics are represented by a virtual queue which is mean rate stable. An online method for real-time dispatch is proposed based on Lyapunov drift algorithm via a drift-minus-revenue function. The upper bound of such a function, which does not depend on future uncertainty, is minimized in each time slot. Explicit dispatch policies are obtained through multi-parametric programming technique so that no optimization problem is solved online. It is proved that the online algorithm can maintain all the constraints across the entire horizon and the expected optimality gap compared to the deterministic offline optimum with perfect uncertainty information is inversely proportional to the weight coefficient in the drift-minus-revenue function. Numerical tests using real wind and electricity price data validate the effectiveness and performance of the proposed method.

In this paper, we present the FC direct trust value-based decision making methodology, for making direct trust value based decisions regarding interactions in (a) a given context and during the current time slot, and (b) a given context and at a future time slot. The direct trust value-based decision making methodology models the context specific nature of trust and the dynamic nature of trust to make direct trust value-based decisions regarding interactions. Additionally in this paper, we present the FC reputation-based trust decision making methodology, for making reputation-based trust decisions regarding interactions, if direct trust value-based decisions cannot be made. The FC reputation-based trust decision making methodology can make reputation-based trust decisions regarding interactions in (a) a given context and during the current time slot, and (b) a given context and at a future time slot.

This paper investigates the collaborative charging problem of plug-in hybrid electric vehicles (PHEVs) in a residential area charging station (CS) under an interaction mechanism. The target is to coordinate the charging strategies of all PHEVs to minimize charging cost of PHEVs on the one hand, and avoid the load peak of CS formed by charging behavior of PHEVs on the other hand. At the same time, the usage habits of PHEV owners, the uncertainty of time-varying electricity price in power market, and operation constraints of PHEVs and CS are also taken into account in this paper. To this end, this paper proposes a data-driven multi-agent PHEVs collaborative charging scheme based on deep reinforcement learning (DRL) method. Firstly, PHEVs are respectively constructed as agents. The interaction among PHEVs in the CS are formulated as a Markov Game. Then, a multi-agent PHEVs charging scheduling algorithm based on a multi-agent deep deterministic policy gradient (MADDPG) method is developed, in which each agent (i.e., PHEV) expects to minimize its cost by choosing the optimal charging strategy during the charging horizon. The proposed method can learn from data mining and gradually grasp the system operation rules by input and output data. Simulations are presented at last to validate the effectiveness of the proposed method.

The key elements in an indirect V2G system architecture are aggregators. They act as an interface between the grid and a group of plug-in hybrid electric vehicles (PHEVs). In this paper, we design an optimal vehicle to grid (V2G) aggregator to control the charging and frequency regulation processes of a group of PHEVs. We consider a problem that an aggregator has to minimize the overall cost of PHEV fleet in a multiple time slot horizon and meet the required battery level when PHEVs plug out. We adopt summation of PHEVs' expenditure in a finite number of time slots as our objective function, which is a quadratic optimization problem. A model predictive control based (MPC-based) PHEV charging and regulation algorithm is proposed to schedule the charging and regulation processes. Through the numerical experiments, we obtain the optimal charging and frequency regulation sequences for each PHEV, the effect of price prediction error on PHEV's cost as well as the impact of penalty factor to plug-out State of Charge (SOC). It is also shown that by taking the optimal control sequences, the PHEV owner can reduce his cost and depart with desired SOC.

On the aggregate grid load imposed by battery health-conscious charging of plug-in hybrid electric vehicles

Energy management strategy of connected hybrid electric vehicles considering electricity and oil price fluctuations: A case study of ten typical cities in China

Electricity-price arbitrage with plug-in hybrid electric vehicle: Gain or loss?