Robust Multi-Period OPF With Storage and Renewables

Jabr, Rabih A.; Karaki, S.; Korbane, Joe Akl

doi:10.1109/tpwrs.2014.2365835

Cited by 164 publications

(99 citation statements)

References 36 publications

(95 reference statements)

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“…In any case, it is possible to compare their results with the ones presented here, without considering the BESS aging costs. In this particular case, the findings of the present papers agree with the ones in [11], as well as showing better cost improvements than the ones found in [8].…”

Section: Discussionsupporting

confidence: 89%

“…The cost results of the optimization are compared with the existing literature, and in particular with papers which assess the cost impact of ESSs using MPOPF methods and quadratic cost functions for balancing generation [8,11]. The resulting cost reduction obtained in [11] ranges between 40% and 50%, with respect to the cost of the system without ESS.…”

Section: Discussionmentioning

confidence: 99%

“…The resulting cost reduction obtained in [11] ranges between 40% and 50%, with respect to the cost of the system without ESS. In the study performed in [8], the authors identified an improvement in cost of around 10% with respect to the case without ESS.…”

Section: Discussionmentioning

confidence: 99%

“…The battery life is defined as L = 1 − D, where D is the remaining capacity of the BESS, normalized to 1. The relation among L and f d is non linear, and includes effects proper of the Li-ion BESSs such as the initial solid electrolyte inter-phase (SEI) film formation by means of parameters α sei and β sei , as given in Equation (11).…”

Section: Battery Cost Modelmentioning

confidence: 99%

“…To overcome this limitation, different studies addressed this issue by proposing multi-period optimal power flow (MPOPF) techniques [8][9][10][11][12][13]. The existing MPOPF-solving techniques include semidefinite programming (SDP) techniques [8][9][10], robust optimization approaches [11], non-linear programming techniques [12], and distributed solving algorithms [13]. However, these approaches fail to address the problem of including the ESS aging costs in the optimization, especially in the case of battery ESSs (BESSs) which are characterized represent the fixed parameters of the model.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Aging Cost Optimization for Planning and Management of Energy Storage Systems

et al. 2017

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In recent years, many studies have proposed the use of energy storage systems (ESSs) for the mitigation of renewable energy source (RES) intermittent power output. However, the correct estimation of the ESS degradation costs is still an open issue, due to the difficult estimation of their aging in the presence of intermittent power inputs. This is particularly true for battery ESSs (BESSs), which have been proven to exhibit complex aging functions. Unfortunately, this collides with considering aging costs when performing ESS planning and management procedures, which are crucial for the exploitation of this technology. In order to overcome this issue, this paper presents the genetic algorithm-based multi-period optimal power flow (GA-MPOPF) procedure, which aims to economically optimize the management of ESSs by taking into account their degradation costs.The proposed methodology has been tested in two different applications: the planning of the correct positioning of a Li-ion BESS in the PG& E 69 bus network in the presence of high RES penetration, and the definition of its management strategy. Simulation results show that GA-MPOPF is able to optimize the ESS usage for time scales of up to one month, even for complex operative costs functions, showing at the same time excellent convergence properties.

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Section: Discussionsupporting

confidence: 89%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Battery Cost Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Aging Cost Optimization for Planning and Management of Energy Storage Systems

et al. 2017

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Algorithms for OptimalACPower Flow in the Presence of Renewable Sources

Chamanbaz

Dabbene

Lagoa

2019

Wiley Encyclopedia of Electrical and Electronics Engineering

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This chapter presents recent solutions to the optimal power flow (OPF) problem in the presence of renewable energy sources (RES), such as solar photo-voltaic and wind generation. After introducing the original formulation of the problem, arising from the combination of economic dispatch and power flow, we provide a brief overview of the different solution methods proposed in the literature to solve it. Then, we explain the main difficulties arising from the increasing RES penetration, and the ensuing necessity of deriving robust solutions. Finally, we present the state-of-the-art techniques, with a special focus on recent methods we developed, based on the application on randomization-based methodologies.

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Joint generation and reserve scheduling of wind‐solar‐pumped storage power systems under multiple uncertainties

Huang

Zhou

Zhang³

et al. 2019

Int Trans Electr Energ Syst

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Summary Due to uncertain nature, large‐scale renewable integration, such as wind and solar energy, has challenged secure operation of power systems. With excellent peak clipping and valley filling capability, pumped storage power is often installed to stabilize fluctuation of renewable power output and improve economy of system operation. This paper aims at exploiting an approach to jointly scheduling generation and reserve for wind‐solar‐pumped storage power systems, taking multiple uncertainties (including wind and solar power output, load change, and generator failure) into account. Uncertainties are treated accordingly by two categories: continuous and discrete. To quantify reserve, continuous uncertainties (forecast errors of wind farm output considering forced outage of wind turbines, solar power output and load) are modeled probabilistically, then discretized into multistate units, further coupled with discrete generator outage to establish capacity outage probability table (COPT) and formulate relationship between reserve and reliability index loss‐of‐load probability (LOLP). Next, a security‐constrained unit commitment (SCUC) model is formulated to handle multiple uncertainties properly, to schedule generation and reserve jointly. Therein, operational characteristics of pumped storage power station are deliberated; stochastic and affinely adjustable robust optimization (AARO) method is adopted to address discrete and continuous uncertainties. Finally, case study is implemented on New England 39‐bus system; it is verified that pumped storage station can effectively improve system economy (accounting for 3.8% of total installed capacity, it can reduce the system cost by 6.9%), proposed that method to coping with multiple uncertainties and transmission constraints is effective, and proposed that approach can assure system reliability in an economic way.

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Robust Multi-Period OPF With Storage and Renewables

Cited by 164 publications

References 36 publications

Aging Cost Optimization for Planning and Management of Energy Storage Systems

Aging Cost Optimization for Planning and Management of Energy Storage Systems

Algorithms for OptimalACPower Flow in the Presence of Renewable Sources

Joint generation and reserve scheduling of wind‐solar‐pumped storage power systems under multiple uncertainties

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