Techno-economic evaluation of PEVs energy storage capability in wind distributed generations planning
Introduction
Energy storage systems (ESSs), in different technologies, can properly accommodate the uncertainty of renewable energy productions. Hence, they can increase the penetration of renewable energies in today’s power systems (Del Pero et al., 2018; Fabien Chidanand, Sisodia, & Gopalan, 2018; Lokeshgupta & Sivasubramani, 2019). Several researchers have investigated the integration of ESSs and renewable energies in both planning and operation studies. (Sedghi, Ahmadian, & Aliakbar-Golkar, 2016) have proposed an optimization algorithm for battery energy storage planning in active distribution networks, where the high penetration of wind and solar powers are considered. (Kong, Kim, Kang, & Jung, 2019) have optimized the size of ESS to maximize its benefit in integration with renewable energies. The government settlement rules and policies are investigated in South Korea and it is considered as a case study. (Oh, Lee, Choi, & Karki, 2019) have evaluated the reliability of a power system in which wind power and multi types of ESSs are integrated. The evaluation procedure is considered as a probabilistic problem and Monte Carlo Simulation is utilized for handing of the uncertainties. The size of a large scale battery is determined optimally based on control strategy by (Cao, Xu, Qin, & Cai, 2020). The smoothing of wind power output is considered as an objective and it is concluded that the output power will more smoothing with lager capacity of battery. Also, the authors have mentioned the efficiency of the battery has only a few effect on battery size. (Saber, Moeini-Aghtaie, Ehsan, & Fotuhi-Firuzabad, 2019) have proposed a scenario-based framework for optimal planning of ESSs. The mitigation of wind power curtailment is considered as the main objective and Non-dominated Sorting Genetic Algorithm II (NSGA II) is utilized to determine the optimal size of ESS. (Ahmadian, Sedghi, Aliakbar-Golkar, Elkamel, & Fowler, 2016) have proposed a probabilistic methodology for battery energy storage planning in a tap-changer equipped distribution network. The wind energy is properly dispatched by the planned energy storage and the operation and planning costs of the system are optimized. However, (Ahmadian, Sedghi, Aliakbar-Golkar, Elkamel et al., 2016; Sedghi, Ahmadian, & Aliakbar-Golkar, 2016) have described the ESSs as very expensive technologies. Accordingly, in order to have an economic feasible utilization of ESSs, several different objectives must be considered, simultaneously. On the other hand, as the plug-in electric vehicles (PEVs) spend about 90–95 % of their lifetime in parking (Ahmadian, Sedghi, Elkamel, Fowler, & Aliakbar Golkar, 2018), they can be used in vehicle to grid (V2G) mode as a mobile energy storage, which are dispersed throughout distribution networks. If we exploit the PEVs for energy storage purposes in electric networks, the investment cost of ESSs will be waived as the PEVs are purchased for transportation purposes. Several researches have been done for optimal coordination of PEVs’ charging/discharging and renewable energies in both planning and operation studies. The PEVs probabilistic behavior and their extra battery degradation in V2G applications are two main challenges in smart charging that should be meticulously modelled. Nevertheless, (Baccino, Grillo, Massucco, & Silvestro, 2015) have modelled the PEVs deterministically that the PEVs data such as home arrival and departure times, driving distance and etc. are considered as certain parameters based on historical data. Moreover, (Lam, Leung, & Li, 2016; Liu, Hu, Song, Wang, & Xie, 2015; Xu & Chung, 2015) have investigated the V2G capabilities for frequency regulation and reliability enhancement. However, the battery degradation cost is not considered in the proposed methodologies. The renewable energy is dispatched using PEVs’ smart charging by (Tushar, Yuen, Huang, Smith, & Poor, 2016), where the PEVs are divided into three categories including premium, conservative and green vehicles. The frequency of two-area interconnected grid that includes wind energy is regulated using V2G operation of PEVs by (Liu, Hu, Song, & Lin, 2013). However, (Liu et al., 2013; Tushar et al., 2016) have considered random state of charge (SOC) for the arrived vehicles, that is resulted random charging demands for the PEVs. The coordination of electric vehicles and wind energy is considered as a virtual power plant by (Abbasi, Taki, Rajabi, Li, & Zhang, 2019), where, a multi-stage risk constraint approach is utilized for optimization. (Mehrjerdi & Hemmati, 2020) have proposed a stochastic model for PEVs charging by wind energy. Although, the uncertainty of renewable energy is considered in the proposed methodology, the uncertainty of PEVs (i.e. driving distance, arrival/departure time, etc.) is not considered. Moreover, the battery degradation cost is not included in the objective function. An optimization methodology is proposed by (Saber & Venayagamoorthy, 2010) for cost-emission minimization of the studied power system, where the charge/discharge power of PEVs is considered as a decision variable. In (Igualada, Corchero, Cruz-Zambrano, & Heredia, 2014) the PEVs charge/discharge power, wind and solar powers are coordinated in order to minimize the daily power fluctuations of renewable energy resources. However, the PEVs driving patterns and their initial SOC are given by (Igualada et al., 2014; Saber & Venayagamoorthy, 2010), so the impact of the driving patterns and times on the studied power systems has been ignored. Based on optimal PEVs charge/discharge scheduling, the energy management algorithm is proposed by (Ma & Mohammed, 2014; Mohamed, Salehi, Ma, & Mohammed, 2014), where the studied power systems contain the renewable resources. A two-stage model is proposed by (Neyestani, Yazdani Damavandi, Shafie-khah, Contreras, & Catalao, 2015) for optimal allocations of PEVs parking lot in distribution networks; in which network constrained objectives are considered. In the proposed methodologies by (Ma & Mohammed, 2014; Mohamed et al., 2014; Neyestani et al., 2015), some distribution network’s constraints such as loading limitation of transformer and grid lines have not taken into account and only PEVs constraints are considered. (Ahmadian, Sedghi, Elkamel, Aliakbar-Golkar, & Fowler, 2017) have proposed a probabilistic methodology for cost-benefit analysis of V2G implementation in distribution networks, where a comprehensive battery degradation model is taken into account. The significant outcome of considering wind energy in implementation of V2G is its high economic impact. In fact, implementation of V2G is not cost effective without considering wind energy. However, (Baccino et al., 2015; Lam et al., 2016; Liu et al., 2015; Xu & Chung, 2015; Liu et al., 2013; Tushar et al., 2016; Igualada et al., 2014; Saber & Venayagamoorthy, 2010; Ma & Mohammed, 2014; Mohamed et al., 2014; Neyestani et al., 2015; Ahmadian, Sedghi, Elkamel et al., 2017) have considered the DGs as the fixed renewable energy resources in which the location and capacity of DGs are predetermined. It must be noted that the utilization of V2G for renewable energy dispatching would be more effective if we optimally determine the DGs capacity based on the available V2G potential. The optimal planning of WDGs is proposed in the literature, (e.g. Ahmadian, Sedghi, Aliakbar-Golkar, Fowler, & Elkamel, 2016), however in that works the PEVs are just considered as an uncertain load and the flexibility of PEVs load and its energy storage capability in V2G operation mode are not taken into account. The PEVs’ energy storage capability has not been evaluated in the reviewed literature (Baccino et al., 2015; Lam et al., 2016; Liu et al., 2015; Xu & Chung, 2015; Liu et al., 2013; Tushar et al., 2016; Igualada et al., 2014; Saber & Venayagamoorthy, 2010; Ma & Mohammed, 2014; Mohamed et al., 2014; Neyestani et al., 2015; Ahmadian, Sedghi, Mohammadi-ivatloo et al., 2017, 2016b). A technical and economic evaluation of PEVs’ energy storage capability in distribution network components planning is identified as the research gap in this field. The main contribution of this work is optimal planning of WDGs based on optimal scheduling of PEVs; that to the best of authors’ knowledge, no study has yet addressed it. The utilization of energy storage capability of PEVs in WDGs planning is the main novelty of this work. This utilization is investigated from technical and economic point of views so that all PEVs’ owners’ restrictions and electric grid constraints are taken into account. In the proposed methodology, the location and capacity of WDGs are determined and charge/discharge power rate and time of PEVs are scheduled optimally. In this paper, a probabilistic approach is proposed for WDGs planning in which the PEVs as the mobile energy storage are optimally scheduled for WDGs dispatching. A new model for the PEVs battery degradation cost is included in the objective function to guarantee the results from technical and economic points of view. As the load demand, power price and wind speed data are different during each season of a year, these data are clustered into four groups. Accordingly, the proposed planning methodology considers the data of each season, separately. As the optimal WDGs planning in distribution network considers several perspectives (such as investment cost, operation cost, technical constraints, etc.), the problem become to a very complicated problem that has several local optimum solutions. This fact makes it to be non-convex. Given that the proposed methodology is a non-convex and nonlinear problem, a hybrid Tabu Search/Particle Swarm Optimization (TS/PSO) optimization algorithm is used for optimal WDGs planning and PEVs scheduling. The proposed methodology is carried out in several scenarios in order to show the capability of V2G for wind energy dispatching and its impact on WDGs planning. The summery of the mentioned explanations, novelty and contributions are presented as follows:
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The energy storage capability of PEVs is utilized for WDGs planning for the first time.
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To reach a more accurate result, the yearly data of wind speed, load demand and electricity price is clustered for four seasons.
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A comprehensive degradation model is considered for PEVs battery to include in the objective function.
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All uncertainties of the input data (i.e. wind speed, load demand, and PEVs load demand) are modelled using PEM method that is an appropriate technique for uncertainty handling.
Section snippets
Problem statement and proposed methodology
In optimal planning of WDGs, capacity and location of turbines should be determined considering the technical constraints. The WDGs planning period depends on the technical and economic parameters of a case study. Wind turbine type, electric grid capacity, and load demand profile are the main parameters that impact on planning period. This period can be varied from 5 to 20 years. For example, the planning period in (Ahmadian, Sedghi, Mohammadi-ivatloo et al., 2017; Novoa & Jin, 2011; Soroudi &
Simulation results
The proposed WDGs optimal planning and PEVs scheduling methodology is applied to a typical 13.8-kV electric distribution grid. This distribution gird consists of 20 load nodes and 1 node that HV/MV substation is installed as shown in Fig. 7. The main parameters of the case study are presented in Table 1. More information about the case study and other technical and economic data can be found in (Carrano, Guimaraes, Takahashi, Neto, & Campelo, 2007). The load demand and wind speed data for four
Future research directions
The linearization of the proposed battery degradation model and power flow equations can be considered as the future research. Moreover, the techno-economic analysis of other types of renewable energies, e.g. solar energy, can be done in the future. In addition, consideration of smart charging station, instead of current distributed smart charging, will be valuable as a new research in the future.
Conclusions
In this paper, a methodology was proposed for optimal wind distributed generations (WDGs) planning subject to optimal scheduling of charge/discharge power of plug-in electric vehicles (PEVs). Three main scenarios include uncoordinated PEVs charging, smart PEVs charging considering battery degradation and smart charging without considering battery degradation were investigated. Based on simulation results, smart charging of PEVs had lower charging cost that is recommended for PEVs scheduling
Declaration of Competing Interest
There is no conflict of interest.
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2023, eTransportationCitation Excerpt :At present, electric vehicles and renewable energy devices have been widely applied all over the world [1–3].