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Optimal portfolio design of energy storage devices with financial and physical right market

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Abstract

With the continuous development of the spot market, in the multi-stage power market environment with the day-ahead market and right market, the study associated with the portfolio of energy storage devices requires that attention should be paid to transmission congestion and power congestion. To maximize the profit of energy storage and avoid the imbalance of power supply and consumption and the risk of node price fluctuation caused by transmission congestion, this paper presents a portfolio strategy of energy storage devices with financial/physical contracts. First, the concepts of financial/physical transmission rights and financial/physical storage rights are proposed. Then, the portfolio models of financial contract and physical contract are established with the conditional value-at-risk to measure the risks. Finally, the portfolio models are verified through the test data of the Pennsylvania-New Jersey-Maryland (PJM) electric power spot market, and the comparison between the risk aversion of portfolios based on financial/physical contract with the portfolio of the market without rights. The simulation results show that the portfolio models proposed in this paper can effectively avoid the risk of market price fluctuations.

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Abbreviations

s ∈ S :

Set of market price scenarios

t ∈ T :

Set of simulation time slots

i ∈ I :

Set of storage profits under each price scenario

c C :

eongestion cost

P T :

Electrical trading volume through congestion branch

l T :

Locational marginal price for injection node

l S :

Locational marginal price for load node

c P :

power congestion cost

P S :

power generation and consumption

F :

Expected profit set of energy storage in some power trading days

φ ∈ [0,1]:

Risk weighting parameter

CVaR α(F):

CVaR of profits

α ∈ [0.9,0.99]:

Confidential level

CVaR α(X):

CVaR of expected profits

\(F_t^{{(^ \ast})}\) :

Expected profit of the day-ahead market

c 0 :

Operation cost per

\(l_{t,(\ast)}^{(\ast)}\) :

Actual marginal price in period t

\(P_t^{(\ast)}\) :

Purchased number in period t

P max :

Maximum input/output power

\(P_{\max}^{(\ast)}\) :

Maximum purchase quantity

P dis :

Energy storage input power

P cha :

Energy storage output power

E t :

Storage capacity at time t

E max :

Maximum storage capacity

E 0 :

Initial storage capacity

λ :

Charging and discharging efficiency of storage

γ :

Normal distribution parameter

ε :

Normal distribution parameter

E 24 + 1 :

Storage capacity at the beginning of the next operation day

E initial :

The initial state of energy storage capacity

P t,(*) :

Bidding or offering in the right market at time t

T h :

Time duration of the transaction of storage

MW:

The capacity of energy storage

MWh:

Power consumption of energy storage

DA:

Day-ahead market

RT:

Real-time market

out:

Discharge

in:

Charge

gen:

Clearing price at the source node

load:

Clearing price at the load node

References

  1. Chinmoy L, Iniyan S, Goic R. Modeling wind power investments, policies and social benefits for deregulated electricity market—a review. Applied Energy, 2019, 242: 364–377

    Article  Google Scholar 

  2. Ghorani R, Fotuhi-Firuzabad M, Moeini-Aghtaie M. Main challenges of implementing penalty mechanisms in transactive electricity markets. IEEE Transactions on Power Systems, 2019, 34(5): 3954–3956

    Article  Google Scholar 

  3. Tudu B, Mandal K K, Chakraborty N. Optimal design and development of PV-wind-battery based nano-grid system: a field-on-laboratory demonstration. Frontiers in Energy, 2019, 13(2): 269–283

    Article  Google Scholar 

  4. Gupta A R, Kumar A. Reactive power deployment and cost benefit analysis in DNO operated distribution electricity markets with D-STATCOM. Frontiers in Energy, 2019, 13(1): 86–98

    Article  Google Scholar 

  5. Li P, Cai G, Zhang Y, et al. Multi-objective optimal allocation strategy for the energy internet in Huangpu District, Guangzhou, China. Frontiers in Energy, 2020, 14(2): 241–253

    Article  Google Scholar 

  6. Vespermann N, Hamacher T, Kazempour J. Access economy for storage in energy communities. IEEE Transactions on Power Systems, 2021, 36(3): 2234–2250

    Article  Google Scholar 

  7. Tenti P, Caldognetto T. A general approach to select location and ratings of energy storage systems in local area energy networks. IEEE Transactions on Industry Applications, 2019, 55(6): 6203–6210

    Article  Google Scholar 

  8. Aguado J A, Quintana V H, Madrigal M, et al. Coordinated spot market for congestion management of inter-regional electricity markets. IEEE Transactions on Power Systems, 2004, 19(1): 180–187

    Article  Google Scholar 

  9. Deng L, Li Z, Sun H, et al. Generalized locational marginal pricing in a heat-and-electricity-integrated market. IEEE Transactions on Smart Grid, 2019, 10(6): 6414–6425

    Article  Google Scholar 

  10. Asrari A, Ansari M, Khazaei J, et al. A market framework for decentralized congestion management in smart distribution grids considering collaboration among electric vehicle aggregators. IEEE Transactions on Smart Grid, 2020, 11(2): 1147–1158

    Article  Google Scholar 

  11. Huang S, Wu Q, Shahidehpour M, et al. Dynamic power tariff for congestion management in distribution networks. IEEE Transactions on Smart Grid, 2019, 10(2): 2148–2157

    Article  Google Scholar 

  12. Han D, Zhang C, Ping J, et al. Smart contract architecture for decentralized energy trading and management based on block-chains. Energy, 2020, 199: 117417

    Article  Google Scholar 

  13. Weibelzahl M. Nodal, zonal, or uniform electricity pricing: how to deal with network congestion. Frontiers in Energy, 2017, 11(2): 210–232

    Article  Google Scholar 

  14. Mahesh A, Sandhu K S. A genetic algorithm based improved optimal sizing strategy for solar-wind-battery hybrid system using energy filter algorithm. Frontiers in Energy, 2020, 14(1): 139–151

    Article  Google Scholar 

  15. Sharma R, Suhag S. Feedback linearization based control for weak grid connected PV system under normal and abnormal conditions. Frontiers in Energy, 2020, 14(2): 400–409

    Article  Google Scholar 

  16. Hartwig K, Kockar I. Impact of strategic behavior and ownership of energy storage on provision of flexibility. IEEE Transactions on Sustainable Energy, 2016, 7(2): 744–754

    Article  Google Scholar 

  17. Paterakis N G, de la Nieta A A S, Bakirtzis A G, et al. Effect of risk aversion on reserve procurement with flexible demand side resources from the ISO point of view. IEEE Transactions on Sustainable Energy, 2017, 8(3): 1040–1050

    Article  Google Scholar 

  18. Saber H, Heidarabadi H, Moeini-Aghtaie M, et al. Expansion planning studies of independent-locally operated battery energy storage systems (BESSs): a CVaR-based study. IEEE Transactions on Sustainable Energy, 2020, 11(4): 2109–2118

    Article  Google Scholar 

  19. Liu X, Yan Z, Wu J. Optimal coordinated operation of a multi-energy community considering interactions between energy storage and conversion devices. Applied Energy, 2019, 248: 256–273

    Article  Google Scholar 

  20. Naderi M, Hashemi F, Bekker A, et al. Modeling right-skewed financial data streams: a likelihood inference based on the generalized Birnbaum-Saunders mixture model. Applied Mathematics and Computation, 2020, 376: 125109

    Article  MathSciNet  MATH  Google Scholar 

  21. Lo Prete C, Guo N, Shanbhag U V. Virtual bidding and financial transmission rights: an equilibrium model for cross-product manipulation in electricity markets. IEEE Transactions on Power Systems, 2019, 34(2): 953–967

    Article  Google Scholar 

  22. Taylor J A. Financial storage rights. IEEE Transactions on Power Systems, 2015, 30(2): 997–1005

    Article  Google Scholar 

  23. Gribik P R, Shirmohammadi D, Graves J S, et al. Transmission rights and transmission expansions. IEEE Transactions on Power Systems, 2005, 20(4): 1728–1737

    Article  Google Scholar 

  24. Baldick R. Border flow rights and contracts for differences of differences: models for electric transmission property rights. IEEE Transactions on Power Systems, 2007, 22(4): 1495–1506

    Article  Google Scholar 

  25. Thomas D, Kazempour J, Papakonstantinou A, et al. A local market mechanism for physical storage rights. IEEE Transactions on Power Systems, 2020, 35(4): 3087–3099

    Article  Google Scholar 

  26. Budworth L, Prestwich A, Sykes-Muskett B, et al. A feasibility study to assess the individual and combined effects of financial incentives and monetary contingency contracts on physical activity. Psychology of Sport and Exercise, 2019, 44: 42–50

    Article  Google Scholar 

  27. Leshno J D, Pradelski B S R. The importance of memory for price discovery in decentralized markets. Games and Economic Behavior, 2021, 125: 62–78

    Article  MathSciNet  MATH  Google Scholar 

  28. Madrigal M, Flores M. Integrated software platform to teach different electricity spot market architectures. IEEE Transactions on Power Systems, 2004, 19(1): 88–95

    Article  Google Scholar 

  29. Gui Z, von Thadden E L, Zhao X. Incentive-compatibility, limited liability and costly liquidation in financial contracting. Games and Economic Behavior, 2019, 118: 412–433

    Article  MathSciNet  MATH  Google Scholar 

  30. Hogan W W. Contract networks for electric power transmission. Journal of Regulatory Economics, 1992, 4(3): 211–242

    Article  Google Scholar 

  31. Muñoz-Álvarez D, Bitar E. Financial storage rights: definition and basic properties. In: 2014 North American Power Symposium (NAPS), Pullman, WA, USA, 2014: 1–6

  32. Quintela F R, Redondo R C, Melchor N R, et al. A general approach to Kirchhoff’s laws. IEEE Transactions on Education, 2009, 52(2): 273–278

    Article  Google Scholar 

  33. Sioshansi R. Using storage-capacity rights to overcome the cost-recovery hurdle for energy storage. IEEE Transactions on Power Systems, 2017, 32(3): 2028–2040

    Article  Google Scholar 

  34. Cui S, Wang Y, Li C, et al. Prosumer community: a risk aversion energy sharing model. IEEE Transactions on Sustainable Energy, 2020, 11(2): 828–838

    Article  Google Scholar 

  35. Palomba G, Riccetti L. Portfolio frontiers with restrictions to tracking error volatility and value at risk. Journal of Banking & Finance, 2012, 36(9): 2604–2615

    Article  Google Scholar 

  36. Catalao J P S, Pousinho H M I, Mendes V M F. Optimal offering strategies for wind power producers considering uncertainty and risk. IEEE Systems Journal, 2012, 6(2): 270–277

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electrical Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China

    Puzhe Lan, Dong Han & Ruimin Zhang

  2. Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Xiaoyuan Xu & Zheng Yan

Authors
  1. Puzhe Lan
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  2. Dong Han
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Correspondence to Dong Han.

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Lan, P., Han, D., Zhang, R. et al. Optimal portfolio design of energy storage devices with financial and physical right market. Front. Energy 16, 95–104 (2022). https://doi.org/10.1007/s11708-021-0788-2

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