Optimal Power Management in Fueled Systems With Finite Storage Capacity

Levron, Yoash; Shmilovitz, Doron

doi:10.1109/tcsi.2009.2037405

Cited by 39 publications

(30 citation statements)

References 30 publications

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“…The primary energy source supplies all of the power that is required for the DC bus, which is the power consumption of the motors p L (t) minus the power supplied by the storage p s (t). As in [19,21], the power system can be represented as shown in Figure 3, where the load, the generated power and the power from the storage are respectively p L (t), p g (t) and p s (t), resulting in the following equation: …”

Section: System Topologymentioning

confidence: 99%

“…It would be possible to calculate the optimal p s (t) assuming a deterministic and well-known load profile [19,20], but in the case discussed in this paper, the load behaviour is stochastic, creating a new challenge.…”

Section: The Loadmentioning

confidence: 99%

“…The only remaining variability is the duration of the load; it is usually unknown (apart from a few ports with advanced terminal operating systems) with the duration being proportional to the height that the container needs to reach in a specific lift cycle, and it depends on the configuration of the stack in the precise location. This paper introduces a novel PMS, which optimises the use of storage under uncertainties on the duration of power loads, unlike previous works that assume full knowledge of the load profile [11,13,19,20]. The aforementioned PMS is tailored for load characterised by known intensity and random duration, representing the power consumption caused by the lifting of containers; nonetheless, it could also be applied to any hybrid system characterised by loads of unknown duration.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Power Management Strategy for Energy Storage with Stochastic Loads

2016

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Abstract:In this paper, a power management strategy (PMS) has been developed for the control of energy storage in a system subjected to loads of random duration. The PMS minimises the costs associated with the energy consumption of specific systems powered by a primary energy source and equipped with energy storage, under the assumption that the statistical distribution of load durations is known. By including the variability of the load in the cost function, it was possible to define the optimality criteria for the power flow of the storage. Numerical calculations have been performed obtaining the control strategies associated with the global minimum in energy costs, for a wide range of initial conditions of the system. The results of the calculations have been tested on a MATLAB/Simulink model of a rubber tyre gantry (RTG) crane equipped with a flywheel energy storage system (FESS) and subjected to a test cycle, which corresponds to the real operation of a crane in the Port of Felixstowe. The results of the model show increased energy savings and reduced peak power demand with respect to existing control strategies, indicating considerable potential savings for port operators in terms of energy and maintenance costs.

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Section: System Topologymentioning

confidence: 99%

Section: The Loadmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal Power Management Strategy for Energy Storage with Stochastic Loads

2016

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“…, for (10) al energy consumed by the local nergy supplied by the grid d to show the smoothness of the the main gird and it is defined ∞ =2 2 _ n n grid P (11) component of the FFT average value of the grid power profile. Power quality values close to 0 me content and therefore better power quality.…”

Section: A Evaluation Criteriamentioning

confidence: 99%

“…Due to the high variability of the renewable resources and the consumption loads, the inclusion of energy storage elements into the microgrid is necessary to regulate the power fluctuations introduced by the renewable sources [9], [10]. In addition, energy management strategies to regulate the energy flow through the microgrid depends on the goals to be achieved [11], [12], [13] and can be performed by the introduction of additional information into the controller (i.e. forecasting).…”

Section: Introductionmentioning

confidence: 99%

Improved fuzzy controller design for battery energy management in a grid connected microgrid

Arcos-Avilés

Espinosa

Guinjoan

et al. 2014

IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society

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An improvement of fuzzy logic controller strategy for a grid-tied domestic microgrid is presented. The fuzzy logic controller takes onto account the microgrid power flux trend in order to minimize the power profile exchanged with the main grid while preventing at the same time the battery life time against deep cycles of charging and discharging. As well as in previous work, the fuzzy logic controller is designed to minimize a set of quality indices. The benefits of the new approach are highlighted through numerical simulations using real generation and consumption measured data.

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A novel peak load shaving algorithm via real-time battery scheduling for residential distributed energy storage systems

Barzkar

Hosseini

2018

Int J Energy Res

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As population grows and energy consumption increases, generation, transmission, and energy distribution costs also increase. Sudden and unpredicted demand increase at peak periods might lead to failure and even damage the power grid. This is a challenge for stability and reliability of the grid. Peak load shaving is considered as an effective approach while transition from peak load periods. In this paper, peak load shaving is modeled mathematically through storing energy on demand side and solved using optimization method. Using the results obtained from solving the optimization problem, a simple effective algorithm is proposed for peak load shaving via real-time scheduling of distributed battery storage systems without complicated calculations. All characteristics required for systemic design of peak load shaving for residential, commercial, and industrial loads are presented. This method can be used in the presence of photovoltaic arrays or other renewable or nonrenewable distributed energy resources simultaneously, and it can be adapted to different conditions and demands. Here, real measured data of a residential state, an office building with photovoltaics, a hotel, and a small office are used for simulation, and GAMS is used for analysis. KEYWORDS battery energy storage system (BESS), DER, distributed energy storage system (DESS), demand side management (DSM), optimization, peak load shaving, peak shifting

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Optimal Power Management in Fueled Systems With Finite Storage Capacity

Cited by 39 publications

References 30 publications

Optimal Power Management Strategy for Energy Storage with Stochastic Loads

Optimal Power Management Strategy for Energy Storage with Stochastic Loads

Improved fuzzy controller design for battery energy management in a grid connected microgrid

A novel peak load shaving algorithm via real-time battery scheduling for residential distributed energy storage systems

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