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One of the most important issues that must be taken into consideration during the planning of energy storage systems (ESSs) is improving distribution network economy, reliability, and stability. This paper presents a two-layer optimization model to determine the optimal siting and sizing of ESSs in the distribution network and their best compromise...
Context in source publication
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This work proposes a new approach for the optimal sitting and sizing of capacitors in Radial Distribution Systems (RDS) with the objective of mitigation losses of the system subject to the constraints. In the present approach new combined approach of Voltage Stability Index (VSI) and Bat Algorithm (BA) are applied to decide the optimum placement of...
Citations
... The optimisation of each surface involved incrementally adding layers until the energy required to split the film stabilised relative to the layer count. This stability indicates that additional layers cease to significantly influence the cleavage energy, as detailed in references [44,45]. ...
... The optimisation of each surface involved incrementally adding layers until the energy required to split the film stabilised relative to the layer count. This stability indicates that additional layers cease to significantly influence the cleavage energy, as detailed in references [44,45]. ...
... In this regard, the collaboration of fuzzy axiomatic design (FAD) and fuzzy entropy weighting (FEW) accurately allows the aforementioned quantitative evaluation of linguistic inputs through their fuzzy natures. Also, the FAD does not need to take into account if dimensions and units of a system are consistent (Feng, 2021), while the FEW prevents fluctuations in numerical values of criteria (Sun et al., 2020). Finally, an efficiency ranking among companies and investment areas was performed using FAD and FEW methods together by following the structured solution procedures given below. ...
... Therefore, in this study, the ICs were weighted through the FEW which uses the information entropy concept to describe the objectivity of information. It impartially determines attribute weights by means of the level of difference in information (Sun et al., 2020), and thus, it prevents potential fluctuations in values of criteria. Despite the aforementioned key integration of two methods, there are only two studies combining them (Ighravwe and Oke, 2017;Feng et al., 2021). ...
Purpose
This study aims to present the financial performance of companies and investment areas in the real estate investment trust (REIT) industry.
Design/methodology/approach
A fuzzy model for financial performance measurement (FM-FPM) was proposed through the collaboration of fuzzy axiomatic design (FAD) and fuzzy entropy weighting (FEW). For the data, financial ratios were used, and their importance and functional requirements were collected via a questionnaire survey.
Findings
The FM-FPM is a beneficial model to be used for a REIT industry based on the structured procedures of FAD and FEW techniques. It can be suitable to regularly evaluate the performance of REITs and their investment areas in financial means, especially in today’s turbulent business environment. The Turkish market that was considered to show the practical applicability of the FM-FPM demonstrated specifically that diversified real estate was found to rank first, followed by mixed-buildings, warehouses, shopping malls and hotels, respectively.
Research limitations/implications
The FM-FPM can be employed for REIT industries in other countries and adapted to different industries. However, more respondents or a different set of criteria might lead to different outputs.
Practical implications
The FM-FPM may guide REIT managers and investors while making their decisions and controlling the performance of REITs and investment areas.
Social implications
The FM-FPM may encourage low- and middle-income investors to make good use of their savings.
Originality/value
The research is first (1) to offer a FPM model in order to determine investable areas in a REIT industry and (2) to employ multiple criteria decision-making tools in order to measure the financial performance of individual companies and investment areas in a REIT industry.
... Article [14] presents a two-layer optimization model using the non-dominated sorting bat algorithm (NSIBA), as well as the Pareto algorithm, which makes it possible to determine the optimal capacity and location of an ESS under the condition of the parallel operation of wind turbines and solar panels with the implementation of "High storage, low generation". The article analyzes the impact of an ESS on the quality of electricity: during modeling, a decrease in electricity losses was achieved, as well as an increase in voltage stability. ...
The unevenness of the electricity consumption schedule at enterprises leads to a peak power increase, which leads to an increase in the cost of electricity supply. Energy storage devices can optimize the energy schedule by compensating the planned schedule deviations, as well as reducing consumption from the external network when participating in a demand response. However, during the day, there may be several peaks in consumption, which lead to a complete discharge of the battery to one of the peaks; as a result, total peak power consumption does not decrease. To optimize the operation of storage devices, a day-ahead forecast is often used, which allows to determine the total number of peaks. However, the power of the storage system may not be sufficient for optimal peak compensation. In this study, a long-term forecast of power consumption based on the use of exogenous parameters in the decision tree model is used. Based on the forecast, a novel algorithm for determining the optimal storage capacity for a specific consumer is developed, which optimizes the costs of leveling the load schedule.
Shared energy storage is an energy storage business application model that integrates traditional energy storage technology with the sharing economy model. Under the moderate scale of investment in energy storage, every effort should be made to maximize the benefits of each main body. In this regard, this paper proposes a distributed shared energy storage double-layer optimal allocation method oriented to source-grid cooperative optimization. First, considering the regulation needs of the power side and the grid side, a distributed shared energy storage operation model is proposed. Second, a distributed shared energy storage double-layer planning model is constructed, with the lowest cost of the distributed shared energy storage system as the upper-layer objective, and the lowest daily integrated operation cost of the distribution grid-distributed new energy stations as the lower-layer objective. Third, a double-layer iterative particle swarm algorithm combined with tide calculation is used to solve the distributed shared energy storage configuration and distribution grid-distributed new energy stations’ economic operation problem. Finally, a comparative analysis of four scenarios verifies that configuring distributed shared energy storage can increase the new energy consumption rate to 100% and reduce the net load peak-valley difference by 61%. Meanwhile, distributed shared energy storage operators have realized positive returns.