Smart energy management algorithm for load smoothing and peak shaving based on load forecasting of an island’s power system

Chapaloglou, Spyridon; Nesiadis, Athanasios; Iliadis, Petros; Ατσόνιος, Κωνσταντίνος; Νikolopoulos, Nikos; Grammelis, Panagiotis; Yiakopoulos, Christos; Antoniadis, Ioannis; Kakaras, Emmanuel

doi:10.1016/j.apenergy.2019.01.102

Cited by 131 publications

(41 citation statements)

References 40 publications

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“…A set of metric indicators were employed to evaluate the proposed algorithm and are listed in Table 1. These indicators were calculated for the three cases: i) no BESS installation in the system as the reference case, ii) the previous algorithm's version [9], and iii) the current improved version. The first indicator was calculated by lowering the signal until its mean value became 0 (by subtracting the mean value) and calculating the standard deviation of the points.…”

Section: Resultsmentioning

confidence: 99%

“…The present section continues the development of the peak shaving optimization algorithm presented in [9,15]. The initial version of the algorithm included a clustering procedure to separate the load profiles with a clear peak during evening hours.…”

Section: Peak Shaving Algorithmmentioning

confidence: 99%

“…In our group's recent publication [9], a small southern-European island power system (yearly load peak ≈ 1.5MW) was examined, composed of Diesel Generators (DG), a 300kWp PV farm and a Battery Energy Storage System (BESS) of 2MWh capacity. A predictive EMS capable of shaving the demand peaks with stored renewable energy, smoothing the operation of the DG and diminishing the ramp ups that occur before the night hours was presented.…”

Section: Introductionmentioning

confidence: 99%

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Advanced energy management system based on PV and load forecasting for load smoothing and optimized peak shaving of islanded power systems

et al. 2019

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Photovoltaic (PV) systems constitute one of the most promising renewable energy sources, especially for warm and sunny regions like the southern-European islands. In such isolated systems, it is important to utilize clean energy in an optimal way in order to achieve high renewable penetration. In this operational strategy, a Battery Energy Storage System (BESS) is most often used to transfer an amount of the stored renewable energy to the peak hours. This study presents an integrated energy management methodology for a PV-BESS energy system targeting to make the load curve of the conventional fuel based units as smooth as possible. The presented methodology includes prediction modules for short-term load and PV production forecasting using artificial neural, and a novel, optimized peak shaving algorithm capable of performing each day’s maximum amount of peak shaving and smoothing level simultaneously. The algorithm is coupled with the overall system model in the Modelica environment, on the basis of which dynamic simulations are performed. The simulation results are compared with the previous version of the algorithm that had been developed in CERTH, and it is revealed that the system’s performance is drastically improved. The overall approach proves that in such islanding systems, a PV-BESS is a suitable option to flatten the load of the conventional fuel based units, achieve steadier operation and increase the share of renewable energy penetration to the grid.

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Section: Resultsmentioning

confidence: 99%

Section: Peak Shaving Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Advanced energy management system based on PV and load forecasting for load smoothing and optimized peak shaving of islanded power systems

et al. 2019

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“…Feras Alasali et al [33] described their energy management algorithm for the energy storage and crane network. Spyridon Chapaloglou et al [34] and J. M. G Lopez [35] presented an energy management algorithm for load flattening and peak-reduction and a simulator for the household energy management system loads, respectively. Yujie Wang et al [36] presented their rule-based energy management strategy based on the power prediction of a lithium-ion battery and a supercapacitor.…”

Section: Related Researchmentioning

confidence: 99%

An Efficient Micro Grid Optimization Theory

Jung

Yoon

2020

Mathematics

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A Micro Grid is an aggregate of many small-scale distributed energy resources (DERs); loads and can be operated independently or together with the existing power grid as a local power grid. The operator of such a grid takes charge of the energy supply and consumption of these resources and loads available in the grid. Meanwhile, the system operator of the grid considers the entire Micro Grid system to be a single load or a generator and assigns the responsibility of its internal management to the operator. The power production from a passive production resource is largely influenced by external environmental factors such as weather conditions, rather than operating conditions. Thus, this study conducted simulations for the cases where four kinds of conditional expressions had not been applied at all or one of them had been applied to compare and evaluate the effectiveness of each expression. As a result, the conditional equations were found to be effective when attempting to optimize the Micro Grids efficiently.

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“…In relation to the mentioned difficulties of the operation of the electricity networks and in pursuance of the control of electricity flows between generation and consumption, battery energy storage systems (BESS) are viewed as a viable option in evening out the fluctuations of the net load curve and in conditioning the renewable energy penetration [57]. Inclusion of BESS into the distribution/underground transmission level of the power supply system is mainly focused on the improvement of the flexibility of the power system by controlling the VER limitation, preventing reverse power flow, maintaining frequency stability, and ensuring the quality of power supply [3,58].…”

Section: Energy Storage Systems In Distribution Networkmentioning

confidence: 99%

Review of Voltage and Reactive Power Control Algorithms in Electrical Distribution Networks

Stanelytė

Radziukynas

2019

Energies

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The traditional unidirectional, passive distribution power grids are rapidly developing into bidirectional, interactive, multi-coordinated smart grids that cover distributed power generation along with advanced information communications and electronic power technologies. To better integrate the use of renewable energy resources into the grid, to improve the voltage stability of distribution grids, to improve the grid protection and to reduce harmonics, one needs to select and control devices with adjustable reactive power (capacitor batteries, transformers, and reactors) and provide certain solutions so that the photovoltaic (PV) converters maintain due to voltage. Conventional compensation methods are no longer appropriate, thus developing measures are necessary that would ensure local reactive and harmonic compensation in case an energy quality problem happens in the low voltage distribution grid. Compared to the centralized methods, artificial intelligence (heuristic) methods are able to distribute computing and communication tasks among control devices.

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Smart energy management algorithm for load smoothing and peak shaving based on load forecasting of an island’s power system

Cited by 131 publications

References 40 publications

Advanced energy management system based on PV and load forecasting for load smoothing and optimized peak shaving of islanded power systems

Advanced energy management system based on PV and load forecasting for load smoothing and optimized peak shaving of islanded power systems

An Efficient Micro Grid Optimization Theory

Review of Voltage and Reactive Power Control Algorithms in Electrical Distribution Networks

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