Feasibility analysis of heterogeneous energy storage technology for cloud energy storage with distributed generation
2019; Institution of Engineering and Technology; Volume: 2019; Issue: 18 Linguagem: Inglês
10.1049/joe.2018.9233
ISSN2051-3305
AutoresN. Nagill, S. Reddy K., Rajesh Kumar, Bijaya Ketan Panigrahi,
Tópico(s)Smart Grid Energy Management
ResumoThe Journal of EngineeringVolume 2019, Issue 18 p. 4970-4974 The 7th International Conference on Renewable Power Generation (RPG 2018)Open Access Feasibility analysis of heterogeneous energy storage technology for cloud energy storage with distributed generation N. Nagill, Corresponding Author N. Nagill nidhinagill4@gmail.com Electrical Engineering, MNIT Jaipur, Rajasthan, IndiaSearch for more papers by this authorS. Reddy K., S. Reddy K. Electrical Engineering, IIT Delhi, IndiaSearch for more papers by this authorR. Kumar, R. Kumar Electrical Engineering, MNIT Jaipur, Rajasthan, IndiaSearch for more papers by this authorB.K. Panigrahi, B.K. Panigrahi Electrical Engineering, IIT Delhi, IndiaSearch for more papers by this author N. Nagill, Corresponding Author N. Nagill nidhinagill4@gmail.com Electrical Engineering, MNIT Jaipur, Rajasthan, IndiaSearch for more papers by this authorS. Reddy K., S. Reddy K. Electrical Engineering, IIT Delhi, IndiaSearch for more papers by this authorR. Kumar, R. Kumar Electrical Engineering, MNIT Jaipur, Rajasthan, IndiaSearch for more papers by this authorB.K. Panigrahi, B.K. Panigrahi Electrical Engineering, IIT Delhi, IndiaSearch for more papers by this author First published: 17 July 2019 https://doi.org/10.1049/joe.2018.9233Citations: 6AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract Fluctuations of electricity prices in demand response schemes and intermittency of renewable energy supplies necessitate the adoption of energy storage in power systems. This paper considers the heterogeneous cloud energy storage (HCES) on cloud energy storage operator side. The goal of this approach is to lower the cost of energy storage by exploiting the different operating characteristics and economics of different battery energy storage technologies. The customers are screened from the type of storage facility that they have purchased and cloud energy storage (CES) operator can maximize the benefit if the complementarily of the storage technologies can be utilized. Distributed generation such as photo-voltaic (PV) generation has also been considered on both side consumer as well as CES operator side for supplying power to energy storage facilities to reduce the cost of charging power. Furthermore, the paper also presents an economic viability of heterogeneous storage system using investment analysis methods. Numerical simulations are carried out based on HCES technological characteristics such as self discharge, round trip charge-discharge cycle efficiency, depth of discharge (DOD) and cycles life time. There is significant changes in the economic performances using HCES when compared with single energy storage. Nomenclature A. Set and indices i Index of consumers S Set of all consumers j Index of batteries H Set of batteries Sces Set of consumers who participate in CES t Index of time intervals T Set of time intervals B. Parameters and constants αt Retail rate of electricity at time instant t ($/kWh) βj,t The export tariff at which electricity from PV generation is fed back to the grid from the jth battery at CES operator side ($/kWh) Di,t Load of user i at time instant t Δt Time interval Ij CES Annualized investment cost of CES equipped with PV generation for battery j Ei cap Energy capacity of ith consumer's battery (kWh) Pi cap Power capacity of ith consumer's battery (kW) Pj cap Power capacity of CES operator's jth battery (kW) Ej cap Energy capacity of CES operator's jth battery (kWh) CPP,CESOp Cost of purchasing electricity from the electric utilities for the CES operator RS,CESOp Revenue for the CES operator from selling electricity from the on-site generation back into the grid and the service fee paid by CES consumers ($) ABC Charging fee paid by the CES consumers to CES operator for charging their cloud batteries ($) Cin Cash inflow Cout Cash outflow C. Decision variables Pi,t C The energy rate at which ith consumer's battery is charged at the time instant t (kW) Pi,t D The energy rate at which ith consumer's battery is discharged at the time instant t (kW) Pj,t C The energy rate at which CES operator's jth battery is charged at the time instant t (kW) Pj,t D The energy rate at which CES operator's jth battery is discharged at the time instant t (kW) Pj,t B,CESOp Power fed back into the grid by CES operator's jth battery at time instant t (kW) D. Function OCCES,op CES operator cost 1 Introduction Energy storage systems are one of the fast growing technologies and have a wide range of applications. They can be used in different ways i.e., from very small domestic PV installations of a few kW to very large pumped hydro of several hundred MW, and from a very short-duration application like frequency response services for grid operators to very long seasonal storage like in hydro reservoirs [1]. Energy storage systems have the potential to solve the challenges like intermittency in the power system due to the integration of renewable sources like solar and wind power [2]. Energy storage systems have some typical applications such as energy arbitrage, frequency regulation, demand shifting and peak shaving. [3]. Energy storage plays an important role in residential and small commercial areas, when there are large differences in energy prices over time. Electric customers can use storage systems in a strategic way to manage their energy use in new and profitable ways. Consumers can avoid paying high rates by storing energy during low-price off-peak periods and releasing that energy when the price is high [4]. Although there is a very large varied subject area of distributed energy storage (DES), some economic and technical limitations hinder their deployment [5, 6]. A centralized management approach for cloud energy storage (CES) has been proposed to address these issues and to better utilize DES and DER resources [7, 8]. As discussed in [9], cloud energy storage is an inventive centrally controlled method of providing virtual storage services to individual consumers, mimicking actual behind-the meter batteries. CES at commercial scale can provide the same quality of service as DES at domestic scale at a substantially lower cost by sharing storage resources and taking advantage of the economies of scale. There are several energy storage technologies which can be used in CES: pumped hydro, compressed air (CAES), super capacitor, flywheel, and various types of flow batteries or solid batteries [10]. In general, these energy storage technologies can be classified into two categories, power application and energy application using the energy to power (E2P) ratio, also known as Discharge Time [11]. The energy storage technologies such as supercapacitor, flywheels, or some types of batteries can absorb or deliver significant amount of power over a short duration can be categorized as power application, if the E2P ratio is about 0.5 h or less. Similarly, the energy storage technologies such as flow batteries, pumped hydro and compressed air energy storage can sustain energy delivery for a much longer period can be categorized as energy application, if the E2P ratio is about 2 h or greater. The cost of these energy storage technologies is high as compared to the price of electricity. However, due to the development in battery technologies their cost is substantially declining [12]. Therefore, heterogeneous energy storage system with different battery technologies (lithium ion battery (Li), lead-acid battery (LA), sodium sulphur battery (NaS) and redox flow battery (ReF)), has been proposed at cloud operator side. The main objective of this approach is to reduce the cost of energy storage by exploiting the different operating characteristics and economics of different battery energy storage technologies. The major contributions of this paper are listed as follows. Economic feasibility analysis of heterogeneous storage system such as lithium-ion battery, lead–acid battery, redox flow battery, sodium sulphur battery (NaS) using investment analysis methods. Extensive simulation studies with respect to the heterogeneous energy storage system with different operating characteristics, photo-voltaic generation and wholesale electricity price (which demonstrates the impact of using heterogeneous energy storage technology over single energy storage, on the financial performance of the CES operator). Rest of the paper is organized as follows. Section 2 presents details of the heterogeneous energy storage technology for cloud energy storage with photo-voltaic generation model used in the study. Section 3 formulates the objective function to minimize the cost and proposes some investment analysis methods used for feasibility analysis of heterogeneous energy storage technology. Section 4 describes case studies based on actual data and analyze the economic feasibility based on simulation results. Section 5 concludes the paper. 2 Cloud energy storage model with heterogeneous storage system CES can be defined as a grid-based storage service that enables ubiquitous and on-demand access to a shared pool of grid-scale energy storage resources [9]. The cloud energy storage presents a feasible technology in deploying bulk energy storage for individual customer benefits. The cloud energy storage (CES) operator would operate the CES as a single entity after attaining the individual customer purchases are finalized. The customers are screened from the type of storage facility that they have purchased and CES operator can maximize the benefit if the complementarily of the storage technologies can be utilized. The structure of heterogeneous storage technology for CES with PV generation is presented in Fig. 1, which consists of four main stakeholders: the CES users, the heterogeneous energy storage facilities (lead–acid battery, lithium ion battery, redox flow battery and sodium Sulphur battery) PV generation and the CES operator. The heterogeneous energy storage technology has been used by the Fig. 1Open in figure viewerPowerPoint HCES with PV Generation CES operator for better utilization of different batteries as there is different technical limitations of different storage technologies. These batteries and battery management systems are not purchased by the consumers. The energy stored in individual batteries is leased to different consumers for a specified interval of time. The basic fundamental is same i.e. to store the electrical energy during low-price off-peak periods and deliver the stored energy when the price of energy is high. 3 Problem formulation 3.1 Economic analysis The flow chart of the economic model of cloud energy storage with heterogeneous technology is presented in Fig. 2. To start with, load profile of consumers, real-time electricity price, energy storage devices used at DES side with its investment cost and the PV generation used to store energy in storage devices and to satisfy load demand. The original electricity bill is then determined by using the data for both cases with and without DES. Then both results are compared, if DES is beneficial to store its optimal charging/ discharging schedule and other optimal parameters. Users that would benefit from DES equipped with PV generation are assumed to purchase CES with heterogeneous technology (HCES) and PV generation and these consumers considered as a set of consumers, Sces. Finally, the optimal charging and discharging schedule of HCES with PV generation using charging and discharging behaviors of each user is evaluated Accordingly, the charging and discharging power of individual storage devices based on its operating characteristics are calculated and the decision on investment and operation of CES is obtained. Fig. 2Open in figure viewerPowerPoint Flow chart of HCES economic analysis 3.2 CES operator cost As discussed in HCES system, the CES operator installs the energy storage facility with PV generation and oversees the flow of money among the CES users, the energy storage facility with PV generation and the electricity market. The CES operator fees include the investment cost of energy storage facilities, the cost of purchasing electricity for charging these energy storage facility used for satisfying the discharging profiles of cloud batteries of CES consumers. It excludes the amount of charging fees transferred by the CES consumers for their cloud batteries to the CES operator and the revenue obtained by CES operator. (1) (2) (3) (4) 3.3 Objective function The main objective is to minimize the CES operator cost considering the revenue obtained by using HCES scheme subjected to some constraints i.e., different technical characteristics such as roundtrip charging-discharging cycle efficiencies, depth of discharge (DOD), and self discharge. The objective function for the CES operator is expressed in (5): (5) subject to: (6) where, is the energy stored in battery from PV generation by CES operator and ηj C /ηj D is the round trip charge-discharge cycle efficiency of jth battery at CES operator side. The service fee paid by the consumers to CES operator must be less than the investment cost of DES to encourage the CES scheme. Note that the optimal schedules of charging and discharging of cloud side storage device are based on aggregated discharge curve of all consumers and electricity price. 3.4 Feasibility analysis Investment methods are used to determine the economic feasibility of any project [13, 14]. HCES with PV generation is considered to be an investment in which some indices used are net revenue, net present value (NPV), discounted payback period (DPBP), internal rate of return (IRR). 3.4.1 Net present value Net present value (NPV) is one of the most widely used index of financial project performance. It can be evaluated from the difference between the present value of cash inflows and outflows [13]. The net present value for HCES system with PV generation can be computed using the relation: (7) where, r is the discount rate and y is the period (in years). Note that, for the initial year cash inflow would be considered zero while cash outflow would be the initial capital investment. 3.4.2 Discounted payback period Payback period is the length of time necessary for project cash flows to refinance the initial investment [13, 14]. DPBP accounts for the time value of money that can be computed by using the relation expressed in (8): (8) where, is the initial investment. 3.4.3 Internal rate of return The internal rate of return (IRR) is a metric used in capital budgeting to estimate the profitability of potential investments. The internal rate of return can be found by equating NPV in (7) to zero and solving for r. (9) 4 Case study 4.1 Data 4.1.1 Load profile and real-time price The load profile is obtained from the region of pjm RTO for the whole year 2016 with a time interval of 60 min [15]. The evaluation is performed using real time prices for pjm RTO region for the whole year 2016 [16]. 4.1.2 PV generation and energy storage The PV generation data obtained for PJM residential consumers for the year of 2016 [17]. The optimal investment and operation of CES equipped with HCES technology and commercial rooftop solar installation that satisfies the aggregated charge and discharge profiles based on the technical characteristics for all consumers are calculated. 4.2 Simulation results The charging and discharging profiles of each battery in both the cases of HCES as well as single storage are based on the electricity price variations as presented in Fig. 3. Fig. 3Open in figure viewerPowerPoint Charging and discharging profiles with real-time price variations The technical parameters used for calculating optimal charging and discharging profiles are presented in Table 1. Table 1. Technical characteristics of different batteries used in HCES [18] Battery Type Energy Rating (MWh) Cycle or lifetime (cycles) @ % DOD Efficiency (%) Capital cost ($/KWh) Lead acid 0.001–40 500–2000 @ 70 70–80 50–150 Lithium-ion 0.001–50 1500–3500 @ 80 75–95 900–1300 Sodium sulphur 0.1–244.8 2500 @ 100 75–89 200–600 Redox flow 2–120 100–13000 @ 75 65–85 600 The batteries store the energy when the electricity price is low and release the energy when the electricity price is high to reduce the electricity cost. The charging and discharging profiles of individual batteries for HCES case, in a typical day are presented in Fig. 4. The results for the whole year for both the cases HCES as well as single battery storage are presented in Fig. 5 and 6 respectively. Fig. 4Open in figure viewerPowerPoint Charging and discharging profiles for individual batteries for HCES case. PC-charging power; PD-discharging power Fig. 5Open in figure viewerPowerPoint Charging and discharging profiles of HCES technology for the whole year Fig. 6Open in figure viewerPowerPoint Charging and discharging profiles of single battery storage for the whole year In the HCES case, the total amount of electricity used is 25.18% less than the total amount of electricity used in case of homogeneous storage over the time of one year. 4.3 Economic feasibility analysis Economic feasibility indices have been calculated using the methodology explained in section 3. The service fees paid by the consumers is the revenue for the CES operator. The service fees are different for individual consumers depend on the maximum charging/discharging power demanded by the consumers. The project life has been considered as 20 years for PV installation. The capital investment cost and the technical parameters of the different energy storage system are presented in Table 1. The DPBP and the cash flow obtained for both cases, HCES and single battery storage technology are presented in Fig. 7 and 8 respectively. It can be noticed from the results that HCES has less DPBP and the positive NPV which indicates the viability of the project. On the other hand, in single battery storage, the DPBP is greater than the project life span and the NPV value and the IRR % is negative which violates the viability condition of any project. Fig. 7Open in figure viewerPowerPoint Cash flow for HCES Fig. 8Open in figure viewerPowerPoint Cash flow for single battery storage 5 Conclusion Heterogeneous energy storage (HES) technology has been implemented on the cloud energy storage operator side. Some investment analysis indicators have been used for analyzing the financial performance of HES technology. Numerical simulations have been carried out based on, HCES operating characteristics such as self-discharge, round trip charge-discharge cycle efficiency, depth of discharge (DOD) and cycles lifetime. Based on the numerical results, it can be observed that the power and energy capacity of each storage technology required is reduced in HCES technology. As the operating characteristics such as cycle lifetime, DOD, round trip charge-discharge cycle efficiency, self-discharge are different for each storage technology in HCES technology, it also influences the financial performance calculations like net revenue, net present value, internal rate of returns, payback period. The latter HCES could be made even more profitable if the power capacity and energy capacity cost are reduced. In the HCES case, the total amount of electricity used is less than the total amount of electricity used in case of homogeneous storage over the time of one year. As the charging and discharging power is reduced, the size of the storage system would be reduced in HCES case and required less space for installation of these different type of batteries. For future work, it is suggested that other energy storage technologies like supercapacitor, compressed air energy storage, pumped storage etc. should also be used for cloud energy storage system to further investigate the system reliability and economic performance. References 1World Energy Resources E-Storage| 2016 World Energy Council: https://www.worldenergy.org/publications/2016/world-energy-resources-2016/ 2Muller S.: ' The power of transformation: wind, sun, and the economics of flexible power systems' ( International Energy Agency, 2014) 3Kousksou T., Bruel P., Jamil A. et al.: 'Energy storage: applications and challenges', Solar Energy Mater. Solar Cells, 2014, 120, pp. 59 – 80 4 Storage, Electrical Energy: ' IEC white paper', 2011 5Zakeri B., Syri S.: 'Electrical energy storage systems: a comparative life cycle cost analysis', Renew. Sustain. Energy Rev., 2015, 42, pp. 569 – 596 6Gomatom P., Jewell W. 'Feasibility evaluation of distributed energy generation and storage for cost and reliability using the 'Worth Factor' criterion'. Frontiers of Power Conf., 2002 7Xu Y., Zhang W., Hug G. et al.: 'Cooperative control of distributed energy storage systems in a microgrid', IEEE Trans. Smart Grid, 2015, 6, (1), pp. 238 – 248 8Pudjianto D., Ramsay C., Strbac G.: 'Virtual power plant and system integration of distributed energy resources', IET Renew. Power Gener., 2007, 1, (1), pp. 10 – 16 9Liu J., Zhang N., Kang C. et al.: 'Cloud energy storage for residential and small commercial consumers: a business case study', Appl. Energy, 2017, 188, pp. 226 – 236 10Koohi-Kamali S., Tyagi V.V., Rahim N.A. et al.: 'Emergence of energy storage technologies as the solution for reliable operation of smart power systems: a review', Renew. Sustain. Energy Rev., 2013, 25, pp. 135 – 165 11Farhadi M., Mohammed O.: 'Energy storage technologies for highpower applications', IEEE Trans. Ind. Appl., 2016, 52, (3), pp. 1953 – 1961 12McDowall J.A.: 'Status and outlook of the energy storage market'. IEEE Power Engineering Society General Meeting, 2007 13Eiffert P.: ' Guidelines for the economic evaluation of building-integrated photovoltaic power systems'. No. NREL/TP-550-31977, National Renewable Energy Lab., Golden, CO, US, 2003 14Ziuku S., Meyer E.L.: 'Economic viability of a residential building integrated photovoltaic generator in South Africa', Int. J. Energy Environ., 2012, 3, (6), pp. 905 – 914 15PJM. Available at http://www.pjm.com/markets-and-operations/ops-analysis.aspx 16PJM. Available at https://dataminer.pjm.com/dataminerui/pages/public/energypricing.jsf 17Solar Resources Data. Available at http://www.pvw-att. nrel.gov/pvwatts.php 18Fathima H., Palanisamy K.: 'Optimized sizing, selection and economic analysis of battery energy storage for grid-connected wind-PV hybrid system', Model. Simul. Eng., 2015, 2015, p. 16 Citing Literature Volume2019, Issue18July 2019Pages 4970-4974 FiguresReferencesRelatedInformation
Referência(s)