Impact of advanced electricity tariff structures on the optimal design, operation and profitability of a grid-connected PV system with energy storage

Springer Science and Business Media LLC - 2019
Lionel Bloch1, Jordan Holweger1, Christophe Ballif1, Nicolas Wyrsch1
1École Polytechnique Fédérale de Lausanne (EPFL), Institute of Microengineering (IMT), Photovoltaics and thin film electronics laboratory (PV-LAB), Rue de la Maladière 71b, Neuchâtel, 2002, Switzerland

Tóm tắt

Abstract The increasing penetration of residential photovoltaics (PV) comes with numerous challenges for distribution system operators. Technical difficulties arise when an excess of PV energy is injected into the grid, causing voltage rise or overloading of the lines. Economic challenges appear because PV owners and consumers are not participating equally in the grid costs. Indeed, PV owners benefit by self-consuming their PV production and by gaining additional revenues when they sell their PV surplus to the grid. Hence, they lower their grid costs. In this paper, we propose a mixed-integer-linear programming approach to solve the design and operation of a PV and battery system efficiently. We use this tool to benchmark five different tariff scenarios, which include real-time pricing, a capacity-based tariff, and a block rate tariff, and evaluate their effect on the design and operation of the system. Carefully tailored metrics show the impact of these tariff structures on the trade-off between the economic viability of privately owned energy systems and their grid usage intensity. Considering both aspects, we show that a block rate tariff is the most promising approach and that capacity-based tariffs rely on PV curtailment alone to curtail the generation peaks.

Từ khóa


Tài liệu tham khảo

Ansari, B, Shi D, Sharma R, Simoes MG (2016) Economic analysis, optimal sizing and management of energy storage for PV grid integration In: Proceedings of the IEEE Power Engineering Society Transmission and Distribution Conference, 1–5.. IEEE, Dallas, TX, USA. https://doi.org/10.1109/TDC.2016.7520090 . http://ieeexplore.ieee.org/document/7520090/ .

Ayompe, LM, Duffy A (2013) Feed-in tariff design for domestic scale grid-connected PV systems using high resolution household electricity demand data. Energy Policy 61:619–627. https://doi.org/10.1016/j.enpol.2013.06.102 .

Azarova, V, Engel D, Ferner C, Kollmann A, Reichl J (2018) Exploring the impact of network tariffs on household electricity expenditures using load profiles and socio-economic characteristics. Nature Energy 3(4):317–325. https://doi.org/10.1038/s41560-018-0105-4 .

Beck, T, Kondziella H, Huard G, Bruckner T (2016) Assessing the influence of the temporal resolution of electrical load and PV generation profiles on self-consumption and sizing of PV-battery systems. Appl Energy 173:331–342. https://doi.org/10.1016/j.apenergy.2016.04.050 .

Bonbright, JC, Danielsen AL, Kamerschen DR (1961) Principles of Public Utility Rates. Columbia University, Press New York.

Babacan, O, Ratnam EL, Disfani VR, Kleissl J (2017) Distributed energy storage system scheduling considering tariff structure, energy arbitrage and solar PV penetration. Appl Energy 205:1384–1393. https://doi.org/10.1016/J.APENERGY.2017.08.025 .

Borenstein, S (2017) Private Net Benefits of Residential Solar PV: The Role of Electricity Tariffs, Tax Incentives, and Rebates. J Assoc Environ Res Econ 4(S1):85–122. https://doi.org/10.1086/691978 .

Bucher, C, Betcke J, Andersson G (2013) Effects of variation of temporal resolution on domestic power and solar irradiance measurements. 2013 IEEE Grenoble Confer PowerTech, POWERTECH 2013 March 2010:1–6. https://doi.org/10.1109/PTC.2013.6652217 .

Darghouth, NR, Wiser RH, Barbose G (2016) Customer economics of residential photovoltaic systems: Sensitivities to changes in wholesale market design and rate structures. Renew Sust Energ Rev 54:1459–1469. https://doi.org/10.1016/J.RSER.2015.10.111 .

Deetjen, TA, Vitter JS, Reimers AS, Webber ME (2018) Optimal dispatch and equipment sizing of a residential central utility plant for improving rooftop solar integration. Energy 147:1044–1059. https://doi.org/10.1016/J.ENERGY.2018.01.110 .

Devine, MT, Farrell N, Lee WT (2017) Optimising feed-in tariff design through efficient risk allocation. Sust Energy, Grids Netw 9:59–74. https://doi.org/10.1016/j.segan.2016.12.003 .

Dutta, G, Mitra K (2017) A literature review on dynamic pricing of electricity. J Oper Res Soc 68(10):1131–1145. https://doi.org/10.1057/s41274-016-0149-4 .

Govaerts, N, Bruninx K, Delarue E (2018) Impact of Distribution Tariff Design on the Profitability of Aggregators of Distributed Energy Storage Systems In: 15th International Conference on the European Energy Market (EEM), 1–5.. IEEE, Łódź, Poland. https://doi.org/10.1109/EEM.2018.8469793 . https://ieeexplore.ieee.org/document/8469793/ .

Gurobi Optimization, L (2019) Gurobi Optimizer 8.1, Reference Manual. http://www.gurobi.com . Accessed 14 Aug 2019.

Heussen, K, Koch S, Ulbig A, Andersson G (2010) Energy storage in power system operation: The power nodes modeling framework. PES Innov Smart Grid Technol Confer Europe, IEEE:1–8. https://doi.org/10.1109/ISGTEUROPE.2010.5638865 .

Hinz, F, Schmidt M, Möst D (2018) Regional distribution effects of different electricity network tariff designs with a distributed generation structure: The case of Germany. Energy Policy 113(November 2017):97–111. https://doi.org/10.1016/j.enpol.2017.10.055 .

Huber, J, Richter B, Weinhardt C (2018) Are consumption tariffs still up-to-date? An operationalized assessment of grid fees In: 15th International Conference on the European Energy Market, EEM, 1–5.. IEEE, Łódź, Poland. https://doi.org/10.1109/EEM.2018.8469847 . https://ieeexplore.ieee.org/document/8469847/ .

IRENA (2017) Electricity Storage and Renewables: Costs and Markets to 2030. https://www.irena.org/publications/2017/Oct/Electricity-storage-and-renewables-costs-and-markets . Accessed 2019-06-12.

IRENA (2016) The power to change: solar and wind cost reduction potential to 2025. Tech Rep. https://www.irena.org/publications/2016/Jun/The-Power-to-Change-Solar-and-Wind-Cost-Reduction-Potential-to-2025 . Accessed 2019-06-12.

Kubli, M (2018) Squaring the sunny circle? On balancing distributive justice of power grid costs and incentives for solar prosumers. Energy Policy 114:173–188. https://doi.org/10.1016/J.ENPOL.2017.11.054 .

Lauinger, D, Caliandro P, Van herle J, Kuhn D (2016) A linear programming approach to the optimization of residential energy systems. J Energy Storage 7:24–37. https://doi.org/10.1016/j.est.2016.04.009 .

Milis, K, Peremans H, Van Passel S (2018) Steering the adoption of battery storage through electricity tariff design. Renew Sust Energ Rev 98(September):125–139. https://doi.org/10.1016/j.rser.2018.09.005 .

Mulder, G, Six D, Claessens B, Broes T, Omar N, Mierlo JV (2013) The dimensioning of PV-battery systems depending on the incentive and selling price conditions. Appl Energy 111:1126–1135. https://doi.org/10.1016/J.APENERGY.2013.03.059 .

O’Shaughnessy, E, Cutler D, Ardani K, Margolis R (2018) Solar plus: Optimization of distributed solar PV through battery storage and dispatchable load in residential buildings. Appl Energy 213:11–21. https://doi.org/10.1016/J.APENERGY.2017.12.118 .

Pena-Bello, A, Burer M, Patel MK, Parra D (2017) Optimizing PV and grid charging in combined applications to improve the profitability of residential batteries. J Energy Storage 13:58–72. https://doi.org/10.1016/J.EST.2017.06.002 .

Ren, Z, Grozev G, Higgins A (2016) Modelling impact of PV battery systems on energy consumption and bill savings of Australian houses under alternative tariff structures. Renew Energy 89:317–330. https://doi.org/10.1016/J.RENENE.2015.12.021 .

Romande, E (2019) Prix. https://www.romande-energie.ch . Accessed 2019-04-12.

Schibuola, L, Scarpa M, Tambani C (2016) Parametric study on the financial performance of battery-supported photovoltaic systems connected to smart grids in current and future market scenarios. Sci Technol Built Environ 22(6):751–765. https://doi.org/10.1080/23744731.2016.1197717 .

Schittekatte, T, Momber I, Meeus L (2018) Future-proof tariff design: Recovering sunk grid costs in a world where consumers are pushing back. Energy Econ 70:484–498. https://doi.org/10.1016/j.eneco.2018.01.028 .

Schreiber, M, Wainstein ME, Hochloff P, Dargaville R (2015) Flexible electricity tariffs: Power and energy price signals designed for a smarter grid. Energy 93:2568–2581. https://doi.org/10.1016/J.ENERGY.2015.10.067 .

SFOECalculateur solaire. https://www.suisseenergie.ch/page/fr-ch/calculateur-solaire . Accessed 2018-01-29.

Simshauser, P (2016) Distribution network prices and solar PV: Resolving rate instability and wealth transfers through demand tariffs. Energy Economics 54:108–122. https://doi.org/10.1016/J.ENECO.2015.11.011 .

Soares, I, Alves MJ, Antunes CH (2019) Designing time-of-use tariffs in electricity retail markets using a bi-level model – Estimating bounds when the lower level problem cannot be exactly solved. Omega. https://doi.org/10.1016/J.OMEGA.2019.01.005 .

Stein, JS, Holmgren WF, Forbess J, Hansen CW (2016) PVLIB: Open source photovoltaic performance modeling functions for Matlab and Python In: 2016 IEEE 43rd Photovoltaic Specialists Conference (PVSC), 3425–3430.. IEEE, Portland, OR, USA.

Stadler, P, Ashouri A, Maréchal F (2016) Model-based optimization of distributed and renewable energy systems in buildings. Energy Build 120:103–113. https://doi.org/10.1016/j.enbuild.2016.03.051 .

Talent, O, Du H (2018) Optimal sizing and energy scheduling of photovoltaic-battery systems under different tariff structures. Renew Energy 129:513–526. https://doi.org/10.1016/j.renene.2018.06.016 .

Theo, WL, Lim JS, Ho WS, Hashim H, Lee CT (2017) Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods. Renew Sust Energy Rev 67:531–573. https://doi.org/10.1016/j.rser.2016.09.063 . arXiv:1011.1669v3 .

Wu, X, Hu X, Yin X, Zhang C, Qian S (2017) Optimal battery sizing of smart home via convex programming. Energy 140:444–453. https://doi.org/10.1016/J.ENERGY.2017.08.097 .

Xu, B, Oudalov A, Ulbig A, Andersson G, Kirschen DS (2018) Modeling of Lithium-Ion Battery Degradation for Cell Life Assessment. IEEE Trans Smart Grid 9(2):1131–1140. https://doi.org/10.1109/TSG.2016.2578950 .

Young, S, Bruce A, MacGill I (2016) Electricity network revenue under different Australian residential tariff designs and customer interventions In: 2016 IEEE Power and Energy Society General Meeting (PESGM), 1–5.. IEEE, Boston, MA, USA. https://doi.org/10.1109/PESGM.2016.7741536 . http://ieeexplore.ieee.org/document/7741536/ .

Young, S, Bruce A, MacGill I (2019) Potential impacts of residential PV and battery storage on Australia’s electricity networks under different tariffs. Energy Policy 128:616–627. https://doi.org/10.1016/J.ENPOL.2019.01.005 .

Zhang, S, Tang Y (2019) Optimal schedule of grid-connected residential PV generation systems with battery storages under time-of-use and step tariffs. J Energy Storage 23:175–182. https://doi.org/10.1016/j.est.2019.01.030 .

Thông tin
Thông tin xuất bản

Springer Science and Business Media LLC

Thông tin tác giả