Methodology for ESS‐type selection and optimal energy management in distribution system with DG considering reverse flow limitations and cost penalties

Rangel, Camilo Alberto Sepúlveda; Canha, Luciane Neves; Sperandio, Maurício; Severiano, Railson

doi:10.1049/iet-gtd.2017.1027

Cited by 35 publications

(24 citation statements)

References 32 publications

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“…P dch,t , u dhc,t , P cha,t , and u cha,t denote the discharge power, discharge state variable, charging power, and charging state variable, respectively. These are decision variables for this problem and are used to calculate P peak through Equation (6). P sch,t denotes the scheduled ESS power.…”

Section: Ess Optimization Model For Minimizing Demand Charges (Stage 1)mentioning

confidence: 99%

“…In a different study, the size, type, and location of ESS were assigned using a genetic algorithm (GA) and then ESS scheduling was performed by including a penalty cost for including a penalty cost for reverse flow. However, the working principle of the ESS regarding the predicted load was not described in detail [6]. Furthermore, a hybrid optimization of ESS in a photovoltaic (PV) integrated electric vehicle (EV) charging station has also been proposed.…”

Section: Introductionmentioning

confidence: 99%

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Development of a Two-Stage ESS-Scheduling Model for Cost Minimization Using Machine Learning-Based Load Prediction Techniques

et al. 2019

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Effective use of energy storage systems (ESS) is important to reduce unnecessary power consumption. In this paper, a day-ahead two-stage ESS-scheduling model based on the use of a machine learning technique for load prediction has been proposed for minimizing the operating cost of the energy system. The proposed algorithm consists of two stages of ESS. In the first stage, ESS is used to minimize demand charges by reducing the peak load. Then, the remaining capacity is used to reduce energy charges through arbitrage trading, thereby minimizing the total operating cost. To achieve this purpose, accurate load prediction is required. Machine learning techniques are promising methods owing to the ability to improve forecasting performance. Among them, ensemble learning is a well-known machine learning method which helps to reduce variance and prevent overfitting of a model. To predict loads, we employed bootstrap aggregating (bagging) or random forest technique-based decision trees after Holt–Winters smoothing for trends. Our combined method can increase the prediction accuracy. In the simulation conducted, three combined prediction models were evaluated. The prediction task was performed using the R programming language. The effectiveness of the proposed algorithm was verified by using Python’s PuLP library.

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Section: Ess Optimization Model For Minimizing Demand Charges (Stage 1)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development of a Two-Stage ESS-Scheduling Model for Cost Minimization Using Machine Learning-Based Load Prediction Techniques

et al. 2019

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“…In particular, the recent progress in computational simulation capacity allowed the introduction of the advanced methodologies in achieving optimal management of renewable systems with ESSs. Though this paper analyzes based on a project with a pre-determined renewable system configuration, Rangel et al (2018) presented a methodology for choosing an optimal size, type, and site of ESS. The purpose of the study was to obtain the optimal charge and discharge strategy and find the best cost option [8].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Though this paper analyzes based on a project with a pre-determined renewable system configuration, Rangel et al (2018) presented a methodology for choosing an optimal size, type, and site of ESS. The purpose of the study was to obtain the optimal charge and discharge strategy and find the best cost option [8]. Ghanaatian and Lotfifard (2018) proposed a method for the optimal control of the flywheel ESS by using a tube-based Model Predictive Control (MPC) model.…”

Section: Literature Reviewmentioning

confidence: 99%

A Scenario Analysis of Solar Photovoltaic Grid Parity in the Maldives: The Case of Malahini Resort

et al. 2018

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The Maldives, one of the Small Island Developing States (SIDS) with great solar potential, is keen to promote renewable energy systems to reduce its heavy reliance on imported diesel for power generation. However, adopting renewable energy systems is still burdensome for the Maldives not only because of its high initial costs and insufficient financial resources but also because of a lack of understanding about whether the deployment of a renewable system is economically feasible. Therefore, the concept of grid parity is explored as an important concept in this paper to examine the possible timeframe for reaching it. A distinctive feature of the paper is that the paper used actual cost and technical information to analyze the levelized cost of energy (LCOEs) of the independent renewable system in a remote island and examined its timeframe for reaching the grid parity condition. Based on economic and technical information from a project for replacing existing diesel generator to photovoltaic (PV) with energy storage system (ESS) in Kuda Bandos Island in the Maldives, the paper considers three different system configurations and evaluates which configuration could result in the most optimal off-grid energy systems in this remote island. With sensitivity analysis on various uncertainties, the paper shows the range of the levelized costs of energy and the periods required for reaching grid parity for deploying solar photovoltaics and ESSs in Kuda Bandos Island, Maldives. The result indicates that the photovoltaic system is an economically feasible option for the resort, and that grid parity can be reached within the project lifetime. However, the result shows that the use of advanced ESSs is still an expensive option and would not be economically reasonable.

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“…The study in [28,29] aims to reduce the total cost and maintain the power quality of the grid integrated with wind generation farms through the deployment of different ESS technologies. This work investigates numerous mathematical frameworks to formulate an optimal ESS placement.…”

Section: Introductionmentioning

confidence: 99%

Minimization of Power Losses through Optimal Battery Placement in a Distributed Network with High Penetration of Photovoltaics

2019

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The problems associated with the deployment of intermittent, unpredictable and uncontrollable solar photovoltaics (PV) can be feasibly solved with battery energy storage systems (BESS), particularly in terms of optimizing the available capacity, increasing reliability and reducing system losses. Consequently, the degree of importance of BESS increases in proportion to the level of PV penetration. Nevertheless, the respective high cost of BESS imposes a huge concern and the need to establish a techno-economic solution. In this paper, we investigate the system losses and power quality issues associated with the high deployment of PV in a grid network and hence formulate BESS capacity optimization and placement methodology based on a genetic algorithm. The concept of the proposed methodology has been tested and validated on a standard IEEE 33 bus system. A brief stepwise analysis is presented to demonstrate the effectiveness and robustness of the proposed methodology in reducing the incremental system losses experienced with increased PV penetration. Furthermore, based on the proposed optimization objectives, a comparative study has also been performed to quantify the impact and effectiveness of aggregated and distributed placement of BESS. The results obtained exhibit a substantial reduction in system losses, particularly in the case of distributed BESS placement.

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Methodology for ESS‐type selection and optimal energy management in distribution system with DG considering reverse flow limitations and cost penalties

Cited by 35 publications

References 32 publications

Development of a Two-Stage ESS-Scheduling Model for Cost Minimization Using Machine Learning-Based Load Prediction Techniques

Development of a Two-Stage ESS-Scheduling Model for Cost Minimization Using Machine Learning-Based Load Prediction Techniques

A Scenario Analysis of Solar Photovoltaic Grid Parity in the Maldives: The Case of Malahini Resort

Minimization of Power Losses through Optimal Battery Placement in a Distributed Network with High Penetration of Photovoltaics

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