Elsevier

Applied Energy

Volume 146, 15 May 2015, Pages 150-161
Applied Energy

Optimal scheduling for vehicle-to-grid operation with stochastic connection of plug-in electric vehicles to smart grid

https://doi.org/10.1016/j.apenergy.2015.02.030Get rights and content

Highlights

  • A novel event-triggered scheduling scheme for vehicle-to-grid (V2G) operation is proposed.

  • New scheme can handle the uncertainty arising from stochastic connection of electric vehicles.

  • New scheme aims at minimizing the overall load variance of power grid by V2G operation.

  • Method to evaluate the performance of proposed scheme is elaborated and demonstrated.

Abstract

Vehicle-to-grid (V2G) operation of plug-in electric vehicles (PEVs) is attracting increasing attention since it can assist to improve the efficiency and reliability of power grid, as well as reduce the operating cost and greenhouse gas emission of electric vehicles. Within the scheme of V2G operation, PEVs are expected to serve as a novel distributed energy storage system (ESS) to help achieve the balance between supply and demand of power grid. One of the key difficulties concerning its practical implementation lies in that the availability of PEVs as ESS for grid remains highly uncertain due to their mobility as transportation tools. To address this issue, a novel event-triggered scheduling scheme for V2G operation based on the scenario of stochastic PEV connection to smart grid is proposed in this paper. Firstly, the mathematical model is formulated. Secondly, the preparation of input data for systematic evaluation is introduced and the case study is conducted. Finally, statistic analysis results demonstrate that our proposed V2G scheduling scheme can dramatically smooth out the fluctuation in power load profiles.

Introduction

Nowadays, energy crisis and global warming have become two critical issues which are threatening the sustainable development of human society. Statistics indicate that the average global temperature has increased by about 0.8 °C since 1880, and two-thirds of the warming has occurred since 1975 [1]. What is more, the exploitable reserves of fossil fuels may be exhausted in the near future due to the rapid growth of global energy consumption. Most recently, transport electrification has been deemed as a promising solution to address these challenges: Firstly, the transport sector accounts for the largest share of the total growth in world consumption of liquid fuels [2]. Secondly, the greenhouse gases (GHGs) emission produced by internal combustion engines has become one of the major contributors to the global warming. The latest IPCC climate change report indicates that the transport sector produced 13% of global GHGs emission [3]. In China, the transport sector produced 709.2 million tones of CO2 in 2012, which accounts for 8.6% of the total 8250.1 million tones of CO2 emission in the same year [4]. Apparently, the popularization of electric vehicles (EVs) will greatly enhance the energy security by integrating renewable energies as well as improving the energy conversion efficiencies. Consequently, the emission of GHGs will be remarkably reduced. In addition, the worries on public health risks arising from the air pollutants including fine particulate matters (PM 2.5) become a powerful incentive to promote EVs in many countries most recently.

Plug-in electric vehicle (PEV) is an important subcategory of EVs. Relatively large-capacity batteries are often equipped in PEVs. In addition, these batteries are rechargeable through plugging into the power grid. Hence, PEVs are a novel kind of electric load for power grid, and on the other hand, they may also play the potential role as distributed energy storage devices for power grid. In this regard, PEVs are able to deposit extra electricity at valley-load hours and then feed back electricity to grid at peak-load hours. The bi-directional power flow between PEVs and power grid is known as vehicle-to-grid (V2G) [5], [6]. Previous research has demonstrated that V2G operation of PEVs can bring in lots of benefits, such as, providing frequency regulation services [7], flattening power load variation [8], reducing overall operating cost [9], [10], [11], promoting the integration of renewable energy sources [12], [13], [14], [15], and reducing the greenhouse gases emissions [16], [17], [18], [19].

The key to the effective implementation of V2G operation is to what extent informatics can be effectively integrated into electrical energy conversion, transmission and distribution. Otherwise, the deep penetration of PEVs may trigger extreme surges in demand at rush hours, and fatally harm the stability and security of the existing power grid. Therefore, V2G should be carried out within the framework of smart grid [20], [21], [22], [23], [24], so that the status information of power grid can be perceived. Another prerequisite is the massive data processing capability, such as cloud computing [25], since there are so much information should be taken into account, for example, the traffic condition, the weather condition, the operation condition of power grid, vehicles and charging facilities, and the demand of PEV owners. The demand of PEV owners should take the top priority among various types of information, and this means that the fundamental function of PEVs as transportation tools has to be guaranteed all the time. Another issue which deserves special attention is the possible degradation of onboard batteries caused by V2G operation [26], [27].

An important theoretical challenge concerning V2G operation is on the optimal charging/discharging strategy of PEVs which aims at maximizing the potential benefits arising from V2G [28]. There are several different targets, such as minimizing the power losses [29], [30], minimizing the peak load [31], controlling the trading risks [32], [33], maximizing the operation profits [34], [35], [36], [37], [38], [39], avoiding the frequency droop [7], [40], minimizing the power load variance [41], and maximizing the integration of renewable energies [42], [43]. The PEV owner’s degree of satisfaction has also been taken into consideration most recently [44]. The equivalence of different optimization targets has been investigated. It indicates that for practical systems, minimizing load variances will minimize power losses approximately, and maximizing load factor is almost equivalent to minimizing the load variance [45].

Essentially speaking, the optimal scheduling for V2G operation is a dynamic programming problem with various constraints ought to be taken into account. Firstly, we should guarantee that there is enough electricity deposited in the onboard battery of PEV for its next itinerary. Secondly, we should make sure that the charging/discharging rate of PEVs will never exceed the capability of its battery and the charging facility. Thirdly, the charging/discharging profiles of PEVs should match the conventional load profile of the power grid, so that the aforementioned benefits of V2G operation can be maximized. It is easy to understand that the optimal scheduling for V2G should be conducted by power grid operators but not the PEVs owners, considering that PEV owners lack sufficient input information and powerful computing resource. Nevertheless, there are also some proposals for V2G implementation in which PEV owners are involved in the scheduling by engaging incentive mechanisms, such as floating electricity prices and bidding strategies [46], [47], [48]. This could be an effective way to promote V2G since it has the potential to reduce the uncertainty and complexity of the scheduling problem by wielding influence on people’s lifestyles. However, in our opinion, it is difficult to achieve optimal scheduling such as minimizing the load variance of power grid. Also, it takes the risk of threatening the security of power grid under a failed biding. In [49], the risks arising from various uncertainties are taken into account when designing bidding strategies.

The purpose of this paper is to propose a novel optimal scheduling strategy for V2G operation based on the scenario of stochastic PEV connection to smart grid. In practical applications, it is quite difficult to acquire the information on the availability of PEVs, viz., when and where the PEVs will be connected to or disconnected from the power grid. To address this issue, the problem formulation regarding minimizing the power load variance with stochastic PEV connection to grid will be elaborated in Section 2. After that, the method to prepare for the input data for systematic evaluation will be introduction in Section 3. Section 4 will be devoted to case study and results analysis. Finally, conclusions will be drawn in Section 5.

Section snippets

General description

Within the scheme of V2G operation, PEVs are expected to serve as a novel distributed energy storage system (ESS) to help achieve the balance between supply and demand of power grid, so as to smooth out the fluctuation of the power load profiles. For most power grids which adopt centralized supervisory control schemes, the grid operators are always eager to find effective measures to keep the total power load curves as flat as possible. Engaging energy storages can greatly suppress the

Preparation of input data for systematic evaluation

In order to evaluate the effectiveness of the proposed scheduling scheme, the input data should be generated to simulate the real situations in a reasonable and indiscriminative way. Nevertheless, some assumptions have to be engaged for simplicity.

Firstly, for all the PEVs involved, some parameters are assumed to be identical: the allowed maximum charging power is set as 4 kW, the allowed minimum and maximum Soc values are set to be 0.2 and 0.8, respectively, and the charging efficiency is set

Case study and results analysis

In order to show how the proposed event-triggered scheduling method works, we depict the whole process step by step in Fig. 6, Fig. 7, Fig. 8. In this case, the number of PEVs involved X is 200 and the preparation of the input data was elaborated in Section 3. As we can see, there are some models not shown herein due to that there is no triggering events happen. For example, there is not any triggering events happening in the second time slot, so the Model (2) is not shown since the

Conclusions

In this paper, a novel optimal scheduling scheme for V2G operation is proposed. One of the key difficulties in the practical implementation of V2G operation lies in that the availability of PEVs as a kind of distributed energy storage system is uncertain due to their mobility as transportation tools. The proposed event-triggered scheduling scheme can solve this problem by updating the optimization model and conducting rescheduling as long as triggering events including PEV connected into grid

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