Techno-economic evaluation of transportable battery energy storage in robust day-ahead scheduling of integrated power and railway transportation networks

https://doi.org/10.1016/j.ijepes.2020.106606Get rights and content

Highlights

  • Transportable Battery Energy Storage is modeled to improve the flexibility of wind-based power system.

  • Coordinated scheduling of TBES and DR program is presented to compensate the wind power variations.

  • An IGDT-based NCUC problem is proposed as a non-probabilistic scheme for uncertainty management.

Abstract

In recent years, the use of renewable energy (RE) sources has an upward trend due to the environmental and economic reasons. However, finding a solution method to manage the fluctuating nature of these sources and more efficient utilization of total generation capacity are challenging problems, especially when there is a high penetration of REs in power systems. On the other side, the network congestion in power grids is another obstacle that inhibits the full utilization of REs. Battery-based energy storage transportation using a railway network leads to emerging high-efficiency technology called transportable battery-based energy storage (TBES) system. TBES technology is a practical and economical option to reduce transmission congestion and increase the utilization of the energy storage systems’ (ESSs’) capacity by providing additional facility to transfer power. As a flexible resource, TBES can adapt to the load profile of the system at peak-load hours and result in cost reduction and more prudent management of wind power variations. The demand response (DR) program is another solution to deal with wind power uncertainty and has a considerable impact on reducing power network congestion and total operation cost by peak-load shaving. Hence, to overcome the mentioned challenges and obstacles, this paper focuses on solving a robust network constrained unit commitment (NCUC) with TBES and DR programs. To manage the wind power uncertainty, an information gap decision theory (IGDT)-based robust optimization technique is proposed to obtain maximum robustness against the wind power uncertainty. The advantage of the presented model is that neither probability distribution functions (PDFs) nor scenario generation are required. The 6-bus power system coordinated with the 3-station railway network is applied as the test system. Numerical studies pointed out that integrating TBES technology in IGDT-based robust NCUC problems and considering the DR program has improved the power system’s flexibility and uncertainty management of wind power, alleviated the congestion, and reduced the optimized cost. Simulation results revealed a 6.5% cost reduction by applying TBES and also 11.3% cost decrement by developing coordinated TBES and DR.

Introduction

As the world industry tends to use environmental-friendly energies instead of fossil fuels to reduce CO2 emission, 21st century is the period of emerging new and challenging energy technologies. REs like solar, wind, and geothermal offer clean types of energy. Wind power is one of the primary sources of REs with high potential. As a report published by the international energy agency (IEA), wind generation capacity in China grew from 15 GW in 2017 to 20 GW in 2018 [1]. According to the U.S. Department of Energy (DOE), it is forecasted that wind energy will support 20% of electricity demand in 2030 in the United States, and will rise to 35% in 2050 [2]. However, the fluctuating nature of this energy, especially when there is high penetration, is one of the significant problems in utilizing this resource in electrical power systems. Hence, the power grid must have the ability to deal with the variable wind energy. Moreover, providing a solution method to utilize maximum wind generation capacity in an efficient way is an important criterion that should be addressed in the operation of power grids.

Recently to manage the fluctuating nature of these kinds of energies and help the power grid to have a secure, reliable, and flexible operation, some technologies like ESSs are recommended. By using ESSs, the surplus amount of produced energy from renewable sources can be stored in low-demand times of the day to be used at peak-load hours. In addition, it can be transferred to the regions with higher energy demand. However, finding a solution method to utilize the maximum available capacity of ESSs is still a challenging problem that system operators confront. On the other side, the new and innovative methods must have the capability to adapt to different aspects of the power grid, such as load profile. Although there are different kinds of ESSs such as pumped-hydro, flywheel, and battery-based energy storage systems (BESSs), researchers prefer to use efficient and fast response technologies. Furthermore, batteries will be a suitable option for this end. The advantages of using BESS include environmental-friendly operation, long life cycle, fast response, and high efficiency [3], [4].

Compensating the demand of the power grid at peak-load hours efficiently is another crucial issue in the daily scheduling of power systems. Transferring large amounts of wind energy from farms to load centers by limited transmission lines may lead to network congestion, especially in conditions with high wind power and low demand [5]. Consequently, optimal scheduling of charging/discharging of the BESSs not only can alleviate transmission congestion but also can provide security and flexibility for power grids with the penetration of wind power [6]. On the other side, to avoid network congestion in transferring the stored energy in ESSs through transmission lines, additional and auxiliary infrastructure for transferring power is required. From the viewpoint of the independent system operator (ISO), BESSs act like load and source due to their ability to charge and discharge. Hence, they should be considered in NCUC formulation for the optimal scheduling of power units to minimize the overall cost.

Mobility of BESSs in electric vehicle (EV) applications can relieve network congestion and reduce the overall costs [7]. Railway systems as one of the major transportation means play a crucial role in the world transportation system. Annually, a huge number of passengers and a considerable amount of cargos are transported by train. This is due to the large potential and also clean and inexpensive transportation offered by the railway systems. Furthermore, the trains can perform similarly to EVs in battery mobility. Battery-based energy storage transportation using a railway system leads to the introduction of a novel technology called TBES system, which is a logical and economical option to decrease transmission congestion and reduce operational costs [8].

To overcome the obstacles of REs penetration, the other solution is to use DR program. The DR program is proposed as a solution method to reduce peak-load by providing a chance for customers to decide the curtailment and shifting schedule of their consumption based on different electricity prices (EPs). Reducing EP, smoothing load profile, improving the wind power uncertainty impact, and lowering the operation cost are some of the advantages of applying this program [9].

As mentioned before the wind power uncertainty is an important issue that should be addressed in the day-ahead scheduling of power systems to obtain a practical and logical model of the power grid. Furthermore, finding methods to model the uncertainties accurately should be of particular interest in power systems’ daily scheduling. Moreover, each introduced method should have the capability to perform efficiently in coordination with other methods. Hence, this paper analyzes the effects of coordinated scheduling of TBES and DR on RE based power systems. In addition, a high-performance non-probabilistic approach is supposed to handle the uncertainty of wind power.

Our brief literature review reveals that the optimal operation of energy storage technologies in the UC problems has attracted a lot of attention from researchers in recent years. ESSs have been used in electric power systems frequently, due to their acceptable results and profitable achievements. However, most researches over ESSs consider these technologies as fixed and stationary sources without transportation capability [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24]. In [10], the performance of micro sources and ESSs in the case of the islanded operation is evaluated. As a result, frequency and voltage regulation and also control capability are improved. In [11], coordinated controllable generation and ESS model, considering technical constraints of ESS and arbitrage opportunities with conventional reserve capacity is proposed using a deterministic optimization method. A thermal unit commitment (UC) with bulk ESSs are modeled in [12] considering various sensitive parameters. Authors in [13] have studied different aspects of using ESSs with the penetration of wind power. In addition, they suggested how to choose an ESS type and its optimal size. Daily scheduling of thermal units with compressed air energy storage (CAES) and wind power is presented in [14]. In [15], the impact of applying BESS on the power system’s security-constrained unit commitment (SCUC) model is evaluated for different levels of wind power penetration. A coordinated operational dispatch planning for a wind farm with BESS is presented in [16], which considered wind speed forecasting errors and battery lifetime. In [17], a stochastic UC model considering the uncertainty of load forecasting in a microgrid (MG), including ESS, renewable generation, diesel generator, and microturbine, is presented. ESS is applied in [18] for combined UC in a wind integrated system to deal with uncertainties. A multi-period probabilistic NCUC model is presented in [19] taking the uncertainty of generation and transmission contingencies into account. In addition, the effect of ESS on the designation of the contingency reserve in the post-contingency states is considered.

In [20], a multi-stage adaptive robust optimization method is proposed for the UC problem with ESS to deal with the variation of wind and solar power. A two-stage robust optimization approach is developed in [21] for UC problem considering the reserve from ESS and wind power intermittency for cost reduction. In [22], an uncertain UC problem coordinated with ESS using a genetic algorithm-priority list-based strategy is presented. In addition, the Taguchi orthogonal arrays technique is adopted to find the robust optimal scheduling of the power grid and minimize the overall cost. The effect of BESS on the vehicle to grid (V2G) applications and evaluating the impact of its mobility on the economy of the power grid are discussed in [23], [24]. In [23], a combined emission-concerned wind-EV study is presented to reduce wind power curtailment, CO2 emission, total operation cost, and EV charging fees. The impact of mobile BESS on congestion and optimized cost of the power system is discussed in [24]. In addition, the effect of the fast charging/discharging of the battery, the depth of discharge (DoD), and ambient temperature on its lifetime have been taken into account.

Evaluating the effects of integrating TBES technology on the operation and control of power systems is presented in [8], [25], [26], [27]. In [8] a battery-based energy storage transportation (BEST) model by railway transportation system is presented that combines SCUC with vehicle routing problem (VRP). The proposed method has alleviated network congestion and operation cost. In [25], a BEST model via the railway system is presented, and the authors introduced a Lagrangian decomposition method to accelerate the convergence of resulting large-scale mixed-integer linear programming (MILP) model. To compensate for the two main challenges for ISO, including system security and reliability, a BEST model considering N-1 contingency is proposed in [26]. To reduce computational time, the presented model is solved by the Benders decomposition (BD) approach in which numerical experiments pointed out that the overall operational cost, transportation cost and locational marginal price are reduced. In [27], to manage the high penetration of wind power, the uncertainties of wind and load are modeled using a scenario-based stochastic method. Numerical results illustrated the positive effect of BEST on integrating high levels of wind energy in the power system. To improve the resilience of the distribution system, a transportable ESS with a two-stage stochastic restoration scheme is presented in [28] to minimize the total cost. In this study, the fluctuations of load and photovoltaic (PV) power have been taken into account.

DR program is introduced for peak shaving, which has been of particular interest in recent studies. An optimization model is presented in [29] considering hourly demand and responses to hourly EPs, to maximize the utility of the consumer. To improve the profit of wind power owners in the day-ahead market, a DR model is presented in [30]. DR programs are introduced as an efficient solution to manage the fluctuating nature of REs in [31]. The cost and profit of DR, the measurement and evolution methods over the impact of DR on EPs are discussed. In [32], the effect of integrating REs with plug-in electric vehicles (PEVs) on MG’s energy management and the overall cost is evaluated. Results pointed out that applying the DR program could be beneficial for both MG and PEV owners. A probabilistic UC model with a pumped-hydro ESS and DR program is proposed in [33] to deal with the wind power integration. To manage the high penetration of REs in power systems, a DR formulation is presented in [34]. The proposed scheme takes the unified unit commitment-economic dispatch coordinated with a generic model into account to improve the participation of DR resources. In [35], a new framework is presented for SCUC considering the DR program in a time shift of air-conditioning load. Numerical results illustrate the positive impact of the proposed model on the ramping demand and economy of the power grid. In [36], the UC problem considering wind uncertainty and DR program is presented using two-stage robust optimization to reduce overall cost and improve wind power generation effects. In the above-mentioned reports coordinated scheduling of TBES and DR is not evaluated.

To precisely manage the variable nature of wind power, an IGDT method can be presented [37]. The IGDT scheduling is a non-probabilistic optimization method that looks for both robust and opportunity approaches to model the uncertainty of wind power. The advantage of this method in comparison to other uncertainty modeling approaches like stochastic or fuzzy method is that neither PDF nor fuzzy logic membership are required to be employed [38].

To model the uncertainties, IGDT has been presented in [38] for bidding strategies of generation companies (GENCOs) considering the uncertainty of EP. In [39], the IGDT optimization approach is developed for managing the load uncertainty. Authors in [40] proposed an IGDT optimization to handle the wind power variation in UC. To model the fluctuations of wind power in the SCUC problem, an IGDT optimization approach is presented in [41]. In addition, transmission switching and ESS are applied for increasing flexibility. Finally, in [42] an IGDT-based SCUC formulation with ESS and DR is proposed, and the results illustrated the validity of the presented model. In the above-mentioned research studies, the IGDT-based robust optimization in the TBES applications was ignored.

Implementing TBES technology can relieve network congestion by providing an auxiliary infrastructure to transmit power. Moreover, this technology can reduce the total operation cost by peak-load shaving and increasing the commitment of the cheapest generation units in power systems. On the other hand, in the case of integrating REs, TBES leads to efficient utilization of wind power by reducing the wind power curtailment and managing wind power fluctuation. In addition, the maximum capacity of ESS can be utilized by using TBES technology. As a result, constructing new generation units and transmission lines would not be required. TBES technology is a flexible method that can adapt to the power system’s load profile and perform efficiently in conditions of high demand power.

Hence, this paper investigates the techno-economic effects of the TBES on the day-ahead scheduling of power systems, which is formulated into an NCUC problem with the penetration of wind power. Additionally, the DR program is adopted for decreasing the EP, smoothing the load profile, decrementing the total operation cost and overcoming the fluctuating nature of RE sources. Coordinated scheduling of DR and TBES can provide more flexibility for optimal operation of power systems. Moreover, to address the wind power uncertainty, the IGDT-based robust optimization approach is developed without the need for PDF. Table 1 illustrates the comparison of the literature with the current proposed model. The advantages and disadvantages of the presented model in the previous studies are provided in this table. To the best of authors’ knowledge, coordinated scheduling of the TBES and DR in the IGDT-based robust NCUC problem of the power systems was not considered in the previous works. Accordingly, the main contributions of this paper are as follows:

  • 1-

    TBES is modeled to improve the flexibility and decrease the congestion of wind-based power system.

  • 2-

    Coordinated scheduling of TBES and DR program is presented to compensate for the wind power variations, increase the flexibility of the system and decrease the overall cost.

  • 3-

    An IGDT-based robust NCUC problem is proposed as a non-probabilistic scheme to deal with wind power fluctuations.

The rest of this paper is organized as follows. The mathematical formulation of IGDT-based NCUC and TBES technology is provided in 2 Deterministic problem formulation, 3 IGDT-based robust NCUC problem formulation. Section 4 represents the case studies. Finally, the conclusion drawn from this study is provided in Section 5.

Section snippets

Objective function

The objective of the proposed IGDT-based robust NCUC model is to minimize the overall cost subjected to railway network and power system constraints. The objective function (1), which is the overall cost, consists of two parts: (1) the total electric power generation cost including the fuel, start-up and shut-down costs of generation units; (2) the total TBES transportation cost and the cost of implementing incentive-based DR program.MintGU[fGU(PGU,t)+SUCGU,t+SDCGU,t]+TBijAsTCTB,ijXTB,ij,s

IGDT-based robust NCUC problem formulation

In this paper, to deal with the uncertainty of wind power generation, the IGDT optimization approach is utilized in NCUC problem. Unlike the stochastic methods, which use PDFs such as Normal, Weibull and Gamma to model the wind power uncertainty [47], in the presented IGDT method, there is no requirement to specify the PDF and fuzzy logic membership. The computational time in IGDT is less than stochastic approaches since there is no need to generate scenarios [48]. In comparison to the

Case studies

In this section, the proposed model is implemented on a 6-bus power system with the related railway network to illustrate the effect of the presented model on the operation and cost optimization of the power system.

Conclusions

In this paper, the effect of TBES technology and DR program on IGDT-based robust NCUC problems of wind-based power systems was evaluated. The presented solution method provided the hourly route of TBES train, charging/discharging pattern of TBES system, scheduling of generation units, optimum robustness function of uncertain variable and the modified load profile. Numerical results demonstrated that utilizing TBES technology increased the utilization of wind power generation and ESS available

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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