Modelling and energy management of a flywheel storage system for peak shaving applications

Tziovani, Lysandros; Hadjidemetriou, Lenos; Charalampous, Charalampos; Timotheou, Stelios; Kyriakides, Elias

doi:10.1109/isgt-europe47291.2020.9248834

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…Towards this direction, an optimization problem is formulated to maximize the arbitrage profit and minimize the square of the transformer violated power, x t , ∀t ∈ T , considering the DC power flow equations to ensure the power flow limits of the distribution grid. The effectiveness of different objectives to penalize the violated power is studied in [39]. The considered problem, denoted by S is given by minimize ∆T t∈T k∈K…”

Section: B Peak Shaving and Energy Arbitragementioning

confidence: 99%

Successive Convexification Algorithms for Optimizing Power Systems With Energy Storage Models

Tziovani,

Hadjidemetriou,

Timotheou

2024

IEEE Trans. Smart Grid

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Energy storage systems (ESSs) are increasingly used in power system optimization. Different ESS mathematical models are developed that consider nonlinear functions for power losses. However, these models require non-convex constraints to represent the ESS losses, resulting in challenging optimization problems. To reduce the complexity, convex relaxation models are often derived but generate infeasible solutions when the relaxation exactness is violated. To deal with this issue, this work develops two successive convexification algorithms that generate fast and high-quality feasible solutions when the derived solution is not exact. The first algorithm handles general loss functions, while the second algorithm enhances performance when piecewise-linear loss functions are used. Specifically, the algorithms reduce the feasible region of the relaxed ESS models using a tightening box trust region around the current solution in successive iterations. The proposed algorithms are applied to the Unit Commitment and Peak Shaving and Energy Arbitrage problems to investigate their performance considering piecewiselinear and quadratic ESS loss functions. Simulation results demonstrate the impact of the ESSs relaxation violation on the actual system operation and validate the algorithms efficacy to generate high-quality feasible and even optimal solutions with significantly lower execution times compared to problems utilizing exact ESS models.

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Section: B Peak Shaving and Energy Arbitragementioning

confidence: 99%

Successive Convexification Algorithms for Optimizing Power Systems With Energy Storage Models

Tziovani,

Hadjidemetriou,

Timotheou

2024

IEEE Trans. Smart Grid

Self Cite

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Minimum loss optimization of flywheel energy storage systems via distributed adaptive dynamic programming

Xiao,

Ding,

Wei

et al. 2024

Optim Control Appl Methods

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In this article, a distributed controller based on adaptive dynamic programming is proposed to solve the minimum loss problem of flywheel energy storage systems (FESS). We first formulate a performance function aiming to reduce total losses of FESS in power distribution applications. Then we use the Hamilton–Jacobi–Bellman (HJB) equation to solve this optimal control problem. The solution of the HJB equation is approximated by neural networks. To achieve distributed control, we estimate the global variables in the HJB equation by using the dynamic average consensus algorithm. A barrier Lyapunov function and a saturation function are introduced to handle the issue of state and input constraints, respectively. Then the stability of the system is proved through the Lyapunov stability analysis. Finally the effectiveness of the proposed strategy is verified by simulations. Simulation results show that FESS can track the power command while minimizing total power losses by interacting with neighbors. The proposed algorithm leads to a loss reduction of compared to the equal power distribution strategy.

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Modelling and energy management of a flywheel storage system for peak shaving applications

Cited by 2 publications

References 15 publications

Successive Convexification Algorithms for Optimizing Power Systems With Energy Storage Models

Successive Convexification Algorithms for Optimizing Power Systems With Energy Storage Models

Minimum loss optimization of flywheel energy storage systems via distributed adaptive dynamic programming

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