Paylaşımlı Elektrik Enerjisi Depolama Sisteminin Kullanımına Dayanan Bir Enerji Yönetimi Yaklaşımı

Year 2019, Issue: 16, 589 - 604, 31.08.2019

Akın Taşcıkaraoğlu Ozan Erdinç

https://doi.org/10.31590/ejosat.574062

Cited By: 1

Abstract

Bu çalışmada, bir paylaşımlı
elektrik enerjisi depolama sistemi kullanan ve aynı bölge içerisinde yer alan
belli bir sayıdaki evsel tüketicinin toplam enerji maliyetini en aza
indirebilmek ve bu evlerin bağlı olduğu dağıtım şebekesindeki pik yük talebini
azaltabilmek amacıyla bir tahmin algoritmasına dayanan bir enerji yönetimi
yaklaşımı önerilmektedir. Önerilen yöntem, farklı güçlerde fotovoltaik (PV)
panellere sahip olan evlere ait elektrik üretimi ve tüketimi miktarlarını, bu
değerlere ait farklı zaman ölçeklerindeki tahminleri ve bir gerçek zamanlı
fiyatlama elektrik tarifesine ait değişken fiyatları dikkate alarak, evler
tarafından üretilen elektrik enerjisinden en yüksek seviyede faydalanmayı
hedeflemektedir. Bu amaçla, her bir eve ait üretim, öncelikle evin kendisinin
tüketimini karşılamak amacıyla kullanılmaktadır. İhtiyaç fazlası üretimin
mevcut olması durumunda ise üretilen enerji belirtilen değişkenler dikkate
alınarak bölge içerisindeki diğer evlerin tüketimi için kullanılmakta,
paylaşımlı depolama sisteminde depolanmakta veya şebekeye satılmaktadır.
Önerilen yaklaşımına göre evler, şebekeye veya bölgedeki diğer evlere sağladıkları
enerji miktarı oranında enerji kredileri kazanmaktadırlar ve bu kredilere karşılık
gelecek miktarda enerjiyi paylaşımlı enerji depolama sisteminden, özellikle
elektrik satın alma fiyatının yüksek olduğu zaman dilimlerinde kullanarak
önemli bir maddi kazanç elde etmektedirler. Belirli bir sayıda evsel tüketiciye
ait gerçek yük talebi ve PV güç üretimi verileri kullanılarak yapılan benzetim
çalışmalarında, paylaşımlı enerji depolama sisteminin var olmadığı durumda elde
edilen sonuçlar ile ve depolama sisteminin var olduğu ancak ilgili tahmin
değerlerinin göz önüne alınmadığı durumda elde edilen sonuçlar ile
karşılaştırmalar yapılmıştır. Belirtilen karşılaştırmalar, önerilen paylaşımlı
enerji depolama sistemi ve tahmin algoritması kullanımına dayanan enerji
yönetimi yaklaşımının son kullanıcı açısından enerji maliyetini azaltmakta ve
dağıtım sistemi işletmecisi açısından pik yük talebini sınırlamakta etkili
olduğunu göstermiştir.  

Keywords

Enerji yönetimi, fotovoltaik güç tahmini, fotovoltaik sistemler, paylaşımlı enerji depolama sistemi, yük talebi tahmini

Supporting Institution

TÜBİTAK

Project Number

117E527

Thanks

Bu çalışma 117E527 No’lu proje kapsamında Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK) tarafından desteklenmektedir.

An Energy Management Approach Based on the Use of Shared Electrical Energy Storage System

Abstract

In this study, an energy management approach
based on a forecasting algorithm is proposed in order to minimize the total
energy cost of a certain number of residential consumers using a shared
electrical energy storage system and located in the same area, and to reduce
the peak load demand in the distribution network to which these houses are
connected. The proposed method aims to utilize the electricity produced by the
houses at the highest level by taking into account the amounts of electricity
production and consumption of houses with photovoltaic (PV) panels of different
powers, forecasts of these values at different time scales ​​and variable
prices of a real-time pricing electricity tariff. For this purpose, the
production of each house is used primarily to meet the consumption of the house
itself. In case of surplus production, the produced energy is used for the
consumption of other houses in the area, stored in the shared storage system or
sold to the network by taking the mentioned variables into account. According
to the proposed approach, houses earn energy credits in proportion to the
amount of energy they provide to the grid or other houses in the area, and they
obtain a significant financial gain by using the corresponding amount of energy
from the shared energy storage system, especially in the periods of high
electricity purchase price. In the simulation studies carried out by using real
load demand and PV power production data for a certain number of residential
consumers, the comparisons are performed with the results obtained in the
absence of a shared energy storage system and the results obtained in case the
storage system exists but the relevant forecast values ​​are not taken into
consideration. These comparisons have shown that the proposed energy management
approach based on the use of shared energy storage system and forecasting
algorithm is effective in reducing the energy cost for the end user and
limiting the peak load demand for the distribution system operator. 

Keywords

Energy management, load demand forecasting, photovoltaic power forecasting, photovoltaic systems, energy storage

Project Number

117E527

References

• Imani, M. H., Ghadi, M. J., Ghavidel, S., & Li, L. (2018). Demand response modeling in microgrid operation: a review and application for incentive-based and time-based programs. Renewable and Sustainable Energy Reviews, 94, 486-499.
• Erdinc, O., Taşcikaraoğlu, A., Paterakis, N. G., & Catalão, J. P. (2018). Novel incentive mechanism for end-users enrolled in DLC-based demand response programs within stochastic planning context. IEEE Transactions on Industrial Electronics, 66(2), 1476-1487.
• Morstyn, T., Hredzak, B., & Agelidis, V. G. (2016). Control strategies for microgrids with distributed energy storage systems: An overview. IEEE Transactions on Smart Grid, 9(4), 3652-3666.
• Erdinç, O., Taşcıkaraoǧlu, A., Paterakis, N. G., Dursun, I., Sinim, M. C., & Catalão, J. P. (2017). Comprehensive optimization model for sizing and siting of DG units, EV charging stations, and energy storage systems. IEEE Transactions on Smart Grid, 9(4), 3871-3882.
• Nghitevelekwa, K., & Bansal, R. C. (2018). A review of generation dispatch with large-scale photovoltaic systems. Renewable and sustainable energy reviews, 81, 615-624.
• Paterakis, N. G., Taşcıkaraoğlu, A., Erdinc, O., Bakirtzis, A. G., & Catalão, J. P. (2016). Assessment of demand-response-driven load pattern elasticity using a combined approach for smart households. IEEE Transactions on Industrial Informatics, 12(4), 1529-1539.
• Siano, P., & Sarno, D. (2016). Assessing the benefits of residential demand response in a real time distribution energy market. Applied Energy, 161, 533-551.
• Taşcıkaraoğlu, A., Paterakis, N.G., Erdinç, O. and Catalao, J.P., 2019. Combining the flexibility from shared energy storage systems and DLC-based demand response of HVAC units for distribution system operation enhancement. IEEE Transactions on Sustainable Energy, 10(1), pp.137-148.
• Taşcıkaraoğlu, A. (2018). Economic and operational benefits of energy storage sharing for a neighborhood of prosumers in a dynamic pricing environment. Sustainable cities and society, 38, 219-229.
• Muratori, M., & Rizzoni, G. (2015). Residential demand response: Dynamic energy management and time-varying electricity pricing. IEEE Transactions on Power systems, 31(2), 1108-1117.
• Nan, S., Zhou, M., & Li, G. (2018). Optimal residential community demand response scheduling in smart grid. Applied Energy, 210, 1280-1289.
• Hu, Q., Li, F., Fang, X., & Bai, L. (2016). A framework of residential demand aggregation with financial incentives. IEEE Transactions on Smart Grid, 9(1), 497-505.
• Asadinejad, A., Rahimpour, A., Tomsovic, K., Qi, H., & Chen, C. F. (2018). Evaluation of residential customer elasticity for incentive based demand response programs. Electric Power Systems Research, 158, 26-36.
• Haider, H. T., See, O. H., & Elmenreich, W. (2016). A review of residential demand response of smart grid. Renewable and Sustainable Energy Reviews, 59, 166-178.
• Yan, X., Ozturk, Y., Hu, Z., & Song, Y. (2018). A review on price-driven residential demand response. Renewable and Sustainable Energy Reviews, 96, 411-419.
• Lu, Q., Yu, H., Zhao, K., Leng, Y., Hou, J., & Xie, P. (2019). Residential demand response considering distributed PV consumption: A model based on China's PV policy. Energy, 172, 443-456.
• Venizelou, V., Philippou, N., Hadjipanayi, M., Makrides, G., Efthymiou, V., & Georghiou, G. E. (2018). Development of a novel time-of-use tariff algorithm for residential prosumer price-based demand side management. Energy, 142, 633-646.
• Iria, J., Soares, F., & Matos, M. (2018). Optimal supply and demand bidding strategy for an aggregator of small prosumers. Applied Energy, 213, 658-669.
• Li, R., Wang, W., Wu, X., Tang, F., & Chen, Z. (2019). Cooperative planning model of renewable energy sources and energy storage units in active distribution systems: A bi-level model and Pareto analysis. Energy, 168, 30-42.
• Shakeri, M., Shayestegan, M., Reza, S. S., Yahya, I., Bais, B., Akhtaruzzaman, M., ... & Amin, N. (2018). Implementation of a novel home energy management system (HEMS) architecture with solar photovoltaic system as supplementary source. Renewable energy, 125, 108-120.
• Wang, G., Zhang, Q., Li, H., McLellan, B. C., Chen, S., Li, Y., & Tian, Y. (2017). Study on the promotion impact of demand response on distributed PV penetration by using non-cooperative game theoretical analysis. Applied energy, 185, 1869-1878.
• Sivaneasan, B., Kandasamy, N. K., Lim, M. L., & Goh, K. P. (2018). A new demand response algorithm for solar PV intermittency management. Applied energy, 218, 36-45.
• Bashir, A., Pourakbari Kasmaei, M., Safdarian, A., & Lehtonen, M. (2018). Matching of local load with on-site PV production in a grid-connected residential building. Energies, 11(9), 2409.
• O'Shaughnessy, E., Cutler, D., Ardani, K., & Margolis, R. (2018). Solar plus: A review of the end-user economics of solar PV integration with storage and load control in residential buildings. Applied energy, 228, 2165-2175.
• Sardi, J., Mithulananthan, N., & Hung, D. Q. (2017). Strategic allocation of community energy storage in a residential system with rooftop PV units. Applied energy, 206, 159-171.
• Wang, Z., Gu, C., & Li, F. (2018). Flexible operation of shared energy storage at households to facilitate PV penetration. Renewable energy, 116, 438-446.
• Gomez-Herrera, J. A., & Anjos, M. F. (2018). Optimal collaborative demand-response planner for smart residential buildings. Energy, 161, 370-380.
• Notton, G., Nivet, M. L., Voyant, C., Paoli, C., Darras, C., Motte, F., & Fouilloy, A. (2018). Intermittent and stochastic character of renewable energy sources: Consequences, cost of intermittence and benefit of forecasting. Renewable and Sustainable Energy Reviews, 87, 96-105.
• Tascikaraoglu, A. (2018). Evaluation of spatio-temporal forecasting methods in various smart city applications. Renewable and Sustainable Energy Reviews, 82, 424-435.
• Córdova, S., Rudnick, H., Lorca, A., & Martínez, V. (2018). An Efficient Forecasting-Optimization Scheme for the Intraday Unit Commitment Process Under Significant Wind and Solar Power. IEEE Transactions on Sustainable Energy, 9(4), 1899-1909.
• Tascikaraoglu, A., & Sanandaji, B. M. (2016). Short-term residential electric load forecasting: A compressive spatio-temporal approach. Energy and Buildings, 111, 380-392.
• Huang, P., & Sun, Y. (2019). A robust control of nZEBs for performance optimization at cluster level under demand prediction uncertainty. Renewable Energy, 134, 215-227.
• Mediwaththe, C. P., Shaw, M., Halgamuge, S. K., Smith, D., & Scott, P. M. (2019). An Incentive-compatible Energy Trading Framework for Neighborhood Area Networks with Shared Energy Storage. IEEE Transactions on Sustainable Energy.
• Liu, J., Zhang, N., Kang, C., Kirschen, D. S., & Xia, Q. (2018). Decision-Making models for the participants in cloud energy storage. IEEE Transactions on Smart Grid, 9(6), 5512-5521.
• Sanandaji, B. M., Tascikaraoglu, A., Poolla, K., & Varaiya, P. (2015, July). Low-dimensional models in spatio-temporal wind speed forecasting. In 2015 American Control Conference (ACC) (pp. 4485-4490). IEEE.
• Pecan Street, Inc. Pecan Street Dataport 2017. <https://dataport.pecanstreet.org>.
• ComEd, https://hourlypricing.comed.com/live-prices/predicted-prices/.