Optimal Planning and Operation Scheduling of Battery Storage Units in Distribution Systems

Mirtaheri, Hamidreza; Bortoletto, Alessandro; Fantino, Maurizio; Mazza, Andrea; Marzband, Mousa

doi:10.1109/ptc.2019.8810421

Cited by 4 publications

(4 citation statements)

References 21 publications

(25 reference statements)

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“…Power flow management can be implemented in technical and economic aspects, or by combining both aspects in the adopted objective function. The optimal selection of microgrid components, i.e., algorithms for solving problems related to the sizing, distribution and operation of storage systems, also plays a significant role [20,21].…”

Section: State Of the Artmentioning

confidence: 99%

Archives of Electrical Engineering

2022

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In recent years, due to the increasing number of renewable energy sources, which are characterised by the stochastic nature of the generated power, interest in energy storage has increased. Commercial installations use simple deterministic methods with low economic efficiency. Hence, there is a need for intelligent algorithms that combine technical and economic aspects. Methods based on computational intelligence (CI) could be a solution. The paper presents an algorithm for optimising power flow in microgrids by using computational intelligence methods. This approach ensures technical and economic efficiency by combining multiple aspects in a single objective function with minimal numerical complexity. It is scalable to any industrial or residential microgrid system. The method uses load and generation forecasts at any time horizon and resolution and the actual specifications of the energy storage systems, ensuring that technological constraints are maintained. The paper presents selected calculation results for a typical residential microgrid supplied with a photovoltaic system. The results of the proposed algorithm are compared with the outcomes provided by a deterministic management system. The computational intelligence method allows the objective function to be adjusted to find the optimal balance of economic and technical effects. Initially, the authors tested the invented algorithm for technical effects, minimising the power exchanged with the distribution system. The application of the algorithm resulted in financial losses, €12.78 for the deterministic algorithm and €8.68 for the algorithm using computational intelligence. Thus, in the next step, a control favouring economic goals was checked using the CI algorithm. The case where charging the storage system from the grid was disabled resulted in a financial benefit of €10.02, whereas when the storage system was allowed to charge from the grid, €437.69. Despite the financial benefits, the application of the algorithm resulted in up to 1560 discharge cycles. Thus,

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Section: State Of the Artmentioning

confidence: 99%

Archives of Electrical Engineering

2022

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“…These can restrict methods to be used for optimization problem calculation [27]. In some works, evolutionary methods are investigated for solving this family of problems [28,29]. In [30], a greedy scheduling method is proposed, where the computational time is critical.…”

Section: Scheduling and Optimizationmentioning

confidence: 99%

Hybrid Forecast and Control Chain for Operation of Flexibility Assets in Micro-Grids

et al. 2021

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Studies on forecasting and optimal exploitation of renewable resources (especially within microgrids) were already introduced in the past. However, in several research papers, the constraints regarding integration within real applications were relaxed, i.e., this kind of research provides impractical solutions, although they are very complex. In this paper, the computational components (such as photovoltaic and load forecasting, and resource scheduling and optimization) are brought together into a practical implementation, introducing an automated system through a chain of independent services aiming to allow forecasting, optimization, and control. Encountered challenges may provide a valuable indication to make ground with this design, especially in cases for which the trade-off between sophistication and available resources should be rather considered. The research work was conducted to identify the requirements for controlling a set of flexibility assets—namely, electrochemical battery storage system and electric car charging station—for a semicommercial use-case by minimizing the operational energy costs for the microgrid considering static and dynamic parameters of the assets.

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“…This module has been developed with the OPAL-RT eMEGAsim Power Grid simulator [21], which allows the behaviour of the power gird to be simulated with a good approximation (see also [22,23]). The SCCE controller receives the energy consumptions and generations from the grid simulation component and, according to the flexibility offered by the assets, defines the optimal operation set-points of the assets [24] (see Section 3).…”

Section: The Planet Dss Toolmentioning

confidence: 99%

Demand Flexibility Enabled by Virtual Energy Storage to Improve Renewable Energy Penetration

et al. 2020

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The increasing resort to renewable energy distributed generation, which is needed to mitigate anthropogenic CO2 emissions, leads to challenges concerning the proper operation of electric distribution systems. As a result of the intrinsic nature of Renewable Energy Sources (RESs), this generation shows a high volatility and a low predictability that make the balancing of energy production and consumption difficult. At the same time, the electrification of new energy-intensive sectors (such as heating) is expected. This complex scenario paves the way for new sources of flexibility that will have more and more relevance in the coming years. This paper analyses how the electrification of the heating system, combined with an electric flexibility utilisation module, can be used to mitigate the problems related to the fluctuating production of RES. By using Power-to-Heat (P2H) technologies, buildings are able to store the overproduction of RES in the form of thermal energy for end-use according to the principle of the so-called Virtual Energy Storage (VES). A context-aware demand flexibility extraction based on the VES model and the flexibility upscale and utilisation on district-level through grid simulation and energy flow optimisation is presented in the paper. The involved modules have been developed within the PLANET (PLAnning and operational tools for optimising energy flows and synergies between energy NETworks) H2020 European project and interact under a unified co-simulation framework with the PLANET Decision Support System (DSS) for the analysis of multi-energy scenarios. DSS has been used to simulate a realistic future energy scenario, according to which the imbalance problems triggered by RES overproduction are mitigated with the optimal exploitation of the demand flexibility enabled by VES.

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Optimal Planning and Operation Scheduling of Battery Storage Units in Distribution Systems

Cited by 4 publications

References 21 publications

Archives of Electrical Engineering

Archives of Electrical Engineering

Hybrid Forecast and Control Chain for Operation of Flexibility Assets in Micro-Grids

Demand Flexibility Enabled by Virtual Energy Storage to Improve Renewable Energy Penetration

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