Smart management of distinct plug‐in hybrid electric vehicle charging stations considering mobility pattern and site characteristics

Management of plug‐in hybrid electric vehicles (PHEVs) is an important alternative energy solution to accord the prevailing environmental depletion. However, adding PHEVs to the existing distribution network may stimulate issues such as increase in peak load, power loss, and voltage deviation. Addressing the aforementioned issues by incorporating distinct mobility patterns together will develop an attractive energy management. In this paper, suitable location of the charging station is presented for a novel 2‐area distribution system following distinct mobility patterns. A comprehensive study by considering the optimal, midst, and unfit site for placing the charging station is incorporated. For managing the charging sequence of PHEVs, a meta‐heuristic solving tool is developed. The main contribution of this programming model is its ability to schedule the vehicles simultaneously in both the areas. The efficiency of the proposed energy management framework is evaluated on the IEEE 33‐bus and IEEE 69‐bus distribution systems. The test system is subjected to different scenarios for demonstrating the superior performance of the proposed solving tool in satisfying the convenience of vehicle owner along with reducing the peak demand. The results show that charging at low electricity price period and discharging at high electricity price period enables the minimum operational cost.     

Simultaneous planning of plug-in hybrid electric vehicle charging stations and wind power generation in distribution networks considering uncertainties

Recently, plug-in hybrid electric vehicles (PHEV) are becoming more attractive than internal combustion engine vehicles (ICEV). Hence, design and modeling of charging stations (CSs) has vital importance in distribution system level. In this paper, a new formulation for PHEV charging stations is presented with the strategic presence of wind power generation (WPG). This study considers constraints of the system losses, the regulatory voltage limits, and the charge/discharge schedule of PHEV based on the social behavior of drivers for appropriate placement of PHEV charging stations in electricity grid. The role of CSs and WPG units must be correctly assessed to optimize the investment and operation cost for the whole system. However, the wind generation owners (WGOs) have different objective functions which might be contrary to the objectives of distribution system manager (DSM). It is assumed that aggregating and management of charge/discharge program of PHEVs are smartly carried out by DSM. This paper presents a long-term bi-objective model for optimal planning of PHEV charging stations and WPG units in distribution systems which simultaneously optimize two objectives, namely the benefits of DSM and WGO. It also considers the uncertainty of load growth, electricity price and PHEV access to the charging station using Mont-Carlo simulation (MCS) method. Initial state of charge uncertainty is also modeled based on scenario approach in PHEV batteries and wind turbine power generation using weibull distribution. Non dominated sorting genetic algorithm (NSGA-II) is used to solve the optimization problem. The simulation has been conducted on the nine-bus system.     

A new modeling and solution method for optimal energy flow in electricity‐gas integrated energy system

With the increasing interdependency of electricity and gas, it is necessary to simultaneously investigate electric power system and natural gas system from the perspective of an electricity‐gas integrated energy system (EGIES). As an extension and integration of both optimal power flow (OPF) and optimal gas flow (OGF), optimal energy flow (OEF) is regarded as the cornerstone of the EGIES and lays an essential foundation for further research on the EGIES's operation and analysis considering stochastic conditions and contingency states. The objective of this paper is to develop a generalized mathematical model and a universally applicable simulation tool for the OEF problem. First, natural gas system is modeled in a way similar to electric power system according to electricity‐gas analogy analysis, where gas admittance, gas nodal admittance matrix, and the nodal equation of gas flow conservation are derived. Then, a generalized accurate OEF model is formulated by simultaneously integrating the OPF model and the OGF model as well as their coupling constraints in a unified modeling framework. Furthermore, an available hybrid optimization approach consisting of whale optimization algorithm, MATPOWER, hydraulic calculation iterative program, and nonstationary penalty function method is put forward to solve the OEF problem. The accuracy, feasibility, and applicability of the proposed modeling and solution method is finally demonstrated by analyzing Belgian 20‐node gas system combined with IEEE 30‐bus test system.     

Effectiveness of smart charging of electric vehicles under power limitations

This article investigates charging strategies for plug‐in hybrid electric vehicles (PHEV) as part of the energy system. The objective was to increase the combined all‐electric mileage (total distance driven using only the traction batteries in each PHEV) when the total charging power at each workplace is subject to severe limitations imposed by the energy system. In order to allocate this power optimally, different input variables, such as state‐of‐charge, battery size, travel distance, and parking time, were considered. The required vehicle mobility was generated using a novel agent‐based model that describes the spatiotemporal movement of individual PHEVs. The results show that, in the case of Helsinki (Finland), smart control strategies could lead to an increase of over 5% in the all‐electric mileage compared to a no‐control strategy. With a high prediction error, or with a particularly small or large battery, the benefits of smart charging fade off. Smart PHEV charging strategies, when applied to the optimal allocation of limited charging power between the cars of a vehicle fleet, seem counterintuitively to provide only a modest increase in the all‐electric mileage. A simple charging strategy based on allocating power to PHEVs equally could thus perform sufficiently well. This finding may be important for the future planning of smart grids as limiting the charging power of larger PHEV fleets will sometimes be necessary as a result of grid restrictions. Copyright © 2013 John Wiley & Sons, Ltd.     

Smart grid adds to renewable resources hosting capacity: Collaboration of plug‐in hybrid electric vehicles in Volt/VAr control process

This paper develops an efficient energy management approach to increase the renewables share in energy provision of smart distribution grids (SDGs). Voltage violation ends in curtailment of renewables generations and, hence, decreases the economic success of distribution companies. To avert such deficits, this study fosters the collaboration of SDG components in an intelligent Volt/VAr control process. The investigated SDG is characterized with high penetration of photovoltaics (PVs), dispatchable distributed generations (DDGs), plug‐in hybrid electric vehicles (PHEVs), and infield control devices say as under‐load tap‐changing transformers (ULTCs). In charge stations, PHEVs are coupled to the SDG through bidirectional inverters which are offering simultaneous exchanges of active and reactive powers. Thus, regarding the PHEV aggregators, optimal schedules of active power charge/discharge signals with their inductive/capacitive reactive power provisions are determined. This notion effectively increases PV power injections and, consequently, provides significant monetary savings. Besides, this mechanism reduces ULTC tap operations in Volt/VAr control process maintaining its nominal lifetime. The proposed approach is formulated as a mixed‐integer non‐linear programming (MINLP) and solved based on DICOPT solvers in general algebraic modeling system (GAMS). Effectiveness of the proposed approach is explored on a typical distribution test system. The obtained results show 8.94% increment in harvested PVs power and hence 5.24% reduction on daily operation cost of SDG.     

PV powered smart charging station for PHEVs

A huge inrush of PHEVs is envisioned in the future. There is a growing risk that, this proliferation in the number of PHEVs will trigger extreme surges in demand while charging them during rush hours. To mitigate this impact, a smart charging station is proposed in which the charging of the PHEVs is controlled in such a way that the impact of charging during peak load period is not felt on the grid. The power needed to charge the plug in hybrids comes from grid-connected photovoltaic generation or the utility or both. The three way interaction between the PV, PHEVs and the grid ensures optimal usage of available power, charging time and grid stability. The system designed to achieve the desired objective consists of a photovoltaic system, DC/DC boost converter, DC/AC bi-directional converter and DC/DC buck converter. The output of DC/DC boost converter and input of DC/AC bi-directional converter share a common DC link. A unique control strategy based on DC link voltage sensing is proposed for the above system for efficient transfer of energy.     

Joint market clearing in a stochastic framework considering power system security

This paper presents a new stochastic framework for provision of reserve requirements (spinning and non-spinning reserves) as well as energy in day-ahead simultaneous auctions by pool-based aggregated market scheme. The uncertainty of generating units in the form of system contingencies are considered in the market clearing procedure by the stochastic model. The solution methodology consists of two stages, which firstly, employs Monte–Carlo Simulation (MCS) for random scenario generation. Then, the stochastic market clearing procedure is implemented as a series of deterministic optimization problems (scenarios) including non-contingent scenario and different post-contingency states. The objective function of each of these deterministic optimization problems consists of offered cost function (including both energy and reserves offer costs), Lost Opportunity Cost (LOC) and Expected Interruption Cost (EIC). Each optimization problem is solved considering AC power flow and security constraints of the power system. The model is applied to the IEEE 24-bus Reliability Test System (IEEE 24-bus RTS) and simulation studies are carried out to examine the effectiveness of the proposed method.     

微网能量管理方案研究

为协调微电网分布式电源、储能单元、负荷与电网之间的能量流及实现系统运行的经济效益最大化,同时保证可靠供电,需要考虑合理可行的能量管理策略,给出了微网能量管理的系统结构图,从能量管理策略、优化目标、约束条件及优化求解算法等四方面优选多种微网能量管理方案,并进行了比较分析。     

Transmission network-based energy and environmental assessment of plug-in hybrid electric vehicles

The introduction of plug-in hybrid electric vehicles (PHEVs) is expected to have a significant impact on regional power systems and pollutant emissions. This paper analyzes the effects of various penetrations of PHEVs on the marginal fuel dispatch of coal, natural gas and oil, and on pollutant emissions of CO₂, NO_x, SO₂ in the New York Metropolitan Area for two battery charging scenarios in a typical summer and winter day. A model of the AC transmission network of the Northeast Power Coordinating Council (NPCC) region with 693 generators is used to realistically incorporate network constraints into an economic dispatch model. A data-based transportation model of approximately 1 million commuters in NYMA is used to determine battery charging pattern. Results show that for all penetrations of PHEVs network-constrained economic dispatch of generation is significantly more realistic than unconstrained cases. Coal, natural gas and oil units are on the margin in the winter, and only natural gas and oil units are on the margin in the summer. Hourly changes in emissions from transportation and power production are dominated by vehicular activity with significant overall emissions reductions for CO₂ and NO_x, and a slight increase for SO₂. Nighttime regulated charging produces less overall emissions than unregulated charging from when vehicles arrive home for the summer and vice versa for the winter. As PHEVs are poised to link the power and transportation sectors, data-based models combining network constraints and economic dispatch have been shown to improve understanding and facilitate control of this link.     

Optimal day-ahead energy planning of multi-energy microgrids considering energy storage and demand response

Due to the environmental and economic advantages of combined heat and power (CHP) units, their use in power grids has expanded. The entry of CHP into power systems increases the complexity of the economic power flow problem. This complexity is due to the introduction of multiple constraints into problem. A mere electricity supply is not optimal in today's networks, and energies such as heat, power and gas must be planned and managed simultaneously as an energy hub. Therefore, in this paper, an intelligent multi-energy microgrid (MG) consisting of power generation units, CHP units and gas units is modeled for day-ahead energy management (DAEM). The economic distribution problem focuses on the amount of power generation, heat and gas of the units in the system. In contrast, the total generation cost of the system is minimized, and all the equality and inequality constraints of the problem are observed. The proposed microgrid includes various energy-dependent equipment such as CHP units, gas boilers, electricity-to-gas units, power and heat storage units and electric heat pumps. Also, price-based load management was included to reduce costs due to the transfer of information between the consumer and the generator in the context of smartization. Since the above problem is difficult to solve due to various constraints and decision parameters, a newly developed optimization method based on water flows was proposed. The simple movement of water flows on the ground is efficient and optimal and always follows the shortest and fastest path to reach the deepest point. In the proposed algorithm, simple movements of water in routing, a change of direction and even the creation of rapids and vortices were simulated as various mathematical operators. Finally, the proposed model and method were examined in different scenarios. The numerical outcomes demonstrated that, the proposed modeling framework is superior to hub-based multi-carrier microgrid models in terms of power system security. The sensitivity of operational expenses to changes in initial values of energy storage systems (ESS) and thermal storage system (TSS) is proved that the cost of operation reduces as the baseline values of ESS and TSS are reduced to 0.2% of the maximum capacity. Because DAEM performance is less flexible when the primary values are reduced by 0.2% of the maximum value, the system running expenses increase marginally.     

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

This article examines the problem of estimating the aggregate load imposed on the power grid by the battery health-conscious charging of plug-in hybrid electric vehicles (PHEVs). The article begins by generating a set of representative daily trips using (i) the National Household Travel Survey (NHTS) and (ii) a Markov chain model of both federal and naturalistic drive cycles. A multi-objective optimizer then uses each of these trips, together with PHEV powertrain and battery degradation models, to optimize both PHEV daily energy cost and battery degradation. The optimizer achieves this by varying (i) the amounts of charge obtained from the grid by each PHEV, and (ii) the timing of this charging. The article finally computes aggregate PHEV power demand by accumulating the charge patterns optimized for individual PHEV trips. The results of this aggregation process show a peak PHEV load in the early morning (between 5.00 and 6.00 a.m.), with approximately half of all PHEVs charging simultaneously. The ability to charge at work introduces smaller additional peaks in the aggregate load pattern. The article concludes by exploring the sensitivity of these results to the relative weighting of the two optimization objectives (energy cost and battery health), battery size, and electricity price.     

Plug-in hybrid electric vehicles as a way to maximize the integration of variable renewable energy in power systems: The case of wind generation in northeastern Brazil

Several studies have proposed different tools for analyzing the integration of variable renewable energy into power grids. This study applies an optimization tool to model the expansion of the electric power system in northeastern Brazil, enabling the most efficient dispatch of the variable output of the wind farms that will be built in the region over the next 20 years. The expected combined expansion of wind generation with conventional inflexible generation facilities, such as nuclear plants and run-of-the-river hydropower plants, poses risks of future mismatch between supply and demand in northeastern Brazil. Therefore, this article evaluates the possibility of using a fleet of plug-in hybrid electric vehicles (PHEVs) to regularize possible energy imbalances. Findings indicate that a dedicated fleet of 500 thousand PHEVs in 2015, and a further 1.5 million in 2030, could be recharged overnight to take advantage of the surplus power generated by wind farms. To avoid the initial costs of smart grids, this article suggests, as a first step, the use of a governmental PHEV fleet that allows fleet managers to control battery charging times. Finally, the study demonstrates the advantages of optimizing simultaneously the power and transport sectors to test the strategy suggested here.     

Investigation of battery end-of-life conditions for plug-in hybrid electric vehicles

Plug-in hybrid electric vehicles (PHEVs) capable of drawing tractive energy from the electric grid represent an energy efficient alternative to conventional vehicles. After several thousand charge depleting cycles, PHEV traction batteries can be subject to energy and power degradation which has the potential to affect vehicle performance and efficiency. This study seeks to understand the effect of battery degradation and the need for battery replacement in PHEVs through the experimental measurement of lithium ion battery lifetime under PHEV-type driving and charging conditions. The dynamic characteristics of the battery performance over its lifetime are then input into a vehicle performance and fuel consumption simulation to understand these effects as a function of battery degradation state, and as a function of vehicle control strategy. The results of this study show that active management of PHEV battery degradation by the vehicle control system can improve PHEV performance and fuel consumption relative to a more passive baseline. Simulation of the performance of the PHEV throughout its battery lifetime shows that battery replacement will be neither economically incentivized nor necessary to maintain performance in PHEVs. These results have important implications for techno-economic evaluations of PHEVs which have treated battery replacement and its costs with inconsistency.     

含储能的三端SOP对主动配电网的潮流优化研究

提出一种含储能的三端智能软开关(SOP)对主动配电网的潮流优化策略,建立以SOP传输的有功功率、无功功率为决策变量,多种约束条件下配电网总有功损耗最小、电压偏差最小的多目标优化数学模型,采用多目标粒子群优化算法求取全局最优解,对SOP的最优接入位置进行选取,实现主动配电网总有功损耗的降低和电压水平的改善.最后,将不同类...     

Hybrid renewable energy‐based distribution system for seasonal load variations

In recent scenario, there is abundant availability of renewable energy resources to satisfy the significant increase in residential, industrial, and commercial demand. This paper presents a novel framework to determine the preeminent size of renewable distributed generators (RDGs) by optimizing the system components such as area required for solar‐photovoltaic modules, swept area occupied by wind turbine blades, and area used by fuel cell. A microgrid with hybrid RDG (h‐RDG) is integrated in distribution system to minimize the distribution loss, substation energy requirement, and improve the voltage level of the load. The power loss minimization is formulated as a nonlinear problem and optimized by the proposed Hybrid Nelder Mead‐Particle Swarm Optimization algorithm. The microgrid location is identified by voltage stability index to improve the stability of system. Further, the system is analyzed for energy flow in different seasonal loading conditions with mixture of residential, industrial, and commercial load. The effective performance of the proposed technique is applied to standard 12‐bus, 69‐bus, and a practical Tamil Nadu (TN) 84‐bus radial distribution system (RDS) for different hybrid combinations of h‐RDG in microgrid. The result proves that the proposed method provides a simple and efficient tool for optimal and flexible use of h‐RDG in microgrid under different climatic changes by simultaneously reducing distribution energy loss and improving voltage profile.     

考虑RE和PHES的网络约束机组组合优化调度

随着新能源(RE)逐渐并网。网络约束混合能源机组的优化调度研究面临巨大挑战。基于RE并网和抽.水蓄能系统(PHES)耦合作用,研究了网络约束下机组组合(UC)优化调度问题。在网络约束的UC模型中,考虑交流网络约束、环境约束、PHES约束及传统的火电机组约束.利用二进制人工羊群算法解决模型求解问题。将网络约束下UC模型分解成1个主问题和1个子问题,主问题决定机组的开关机状态和负荷分配,子问题检查交流电传输的网络约束。以1个包含风电和光伏机组的IEEE-30节点系统来验证RE和PHES的共同影响,结果表明RE影响了系统的运行成本和电力系统的安全性,PHES对抑制RE的消极影响具有重要作用,能够调节RE的不确定性带来的误差。     

Fuzzy stochastic long-term model with consideration of uncertainties for deployment of distributed energy resources using interactive honey bee mating optimization

This paper presents a novel modified interactive honey bee mating optimization (IHBMO) base fuzzy stochastic long-term approach for determining optimum location and size of distributed energy resources (DERs). The Monte Carlo simulation method is used to model the uncertainties associated with long-term load forecasting. A proper combination of several objectives is considered in the objective function. Reduction of loss and power purchased from the electricity market, loss reduction in peak load level and reduction in voltage deviation are considered simultaneously as the objective functions. First, these objectives are fuzzified and designed to be comparable with each other. Then, they are introduced into an IHBMO algorithm in order to obtain the solution which maximizes the value of integrated objective function. The output power of DERs is scheduled for each load level. An enhanced economic model is also proposed to justify investment on DER. An IEEE 30-bus radial distribution test system is used to illustrate the effectiveness of the proposed method.     

Genetic single objective optimisation for sizing and allocation of renewable DG systems

The optimal design of renewable-based distributed generations (DGs) is a challenging issue in order to maximise their benefits and to overcome power quality problems. Therefore, this paper proposes a methodology for optimal allocation and sizing of renewable DG units to minimise total power losses over radial distribution systems. The planning problem is formulated as a single objective nonlinear mixed integer-constrained optimisation problem and is solved by using the augmented Lagrangian genetic algorithm (ALGA) by combining the objective function and the nonlinear constraints. In that case, the ALGA solves a sequence of sub-problems where the objective function penalises the constraints violation in order to obtain the best solution. The proposed technique is applied to IEEE radial test systems including 33-bus, 69-bus and 119-bus and is implemented with different scenarios including all possible combinations and various types of renewable DG units to prove the effectiveness of the proposed methodology.     

An approach to locational marginal price based zonal congestion management in deregulated electricity market

Congestion of transmission line is a vital issue and its management pose a technical challenge in power system deregulation. Congestion occurs in deregulated electricity market when transmission capacity is not sufficient to simultaneously accommodate all constraints of power transmission through a line. Therefore, to manage congestion, a locational marginal price (LMP) based zonal congestion management approach in a deregulated electricity market has been proposed in this paper. As LMP is an economic indicator and its difference between two buses across a transmission line provides the measure of the degree of congestion, therefore, it is efficiently and reliably used in deregulated electricity market for congestion management. This paper utilizes the difference of LMP across a transmission line to categorize various congestion zones in the system. After the identification of congestion zones, distributed generation is optimally placed in most congestion sensitive zones using LMP difference in order to manage congestion. The performance of the proposed methodology has been tested on the IEEE 14-bus system and IEEE 57-bus system.     

A novel real‐time energy management strategy for plug‐in hybrid electric vehicles based on equivalence factor dynamic optimization method

The universal adaptive equivalent consumption minimization strategy (A‐ECMS) has the potential of being implemented in real‐time for plug‐in hybrid electric vehicles (PHEVs). However, the imprecise prediction of a long‐term future driving cycle and biggish computation burdens remain the barriers for further real vehicle application. Thus, it is of great significance to develop a real‐time optimal energy management strategy for PHEVs by weakening the influence of future driving cycle to the control accuracy and improving its computation efficiency. In this paper, a novel real‐time energy management strategy for PHEVs based on equivalence factor (EF) dynamic optimization method is proposed. Firstly, a novel proportional plus integral adaption law for calculating the dynamic optimal EF is established for A‐ECMS using only instantaneous information of current vehicle speed and battery state of charge. Second, three key coefficients are obtained and converted into a three‐dimensional look up tables, so as to determine the dynamic optimal EF. Finally, the method of fast searching the optimal engine torque is proposed, which significantly enhances the computational efficiency. Compared with A‐ECMS, the computational time of A‐ECMS2 is decreased near 94.8% and the deviation of fuel consumption is controlled within 4.4%. Both the numerical results and hardware‐in‐loop results prove that the proposed novel energy management strategy A‐ECMS2 has better real‐time performance and less computing burden than the general A‐ECMS.