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

Park, Minsu; Kim, Jaehwi; Won, Dong-Jun; Kim, Jaehee

doi:10.3390/pr7060370

Cited by 8 publications

(11 citation statements)

References 19 publications

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“…However, they verify that the proposed methods may be feasible in the near future by changing the conditions of the current DR. In [18], a two-phase ESS scheduling model has been introduced. In the first stage of the proposed model, ESS reduces peak load, and in the second stage, electricity trading is performed, which results in a minimization of the overall operating cost of the system by the use of the remaining capacity.…”

Section: Introductionmentioning

confidence: 99%

“…As a result, a feed-forward artificial neural network (FF-ANN) has been introduced in order to predict day-ahead weather conditions instead of using historical data for PV and WT productions. However, considering a weather predicting module in the EMS of the prosumer was neglected in recent studies [18,19]. As a matter of fact, neglecting uncertainties of weather parameter in the day-ahead operation of the prosumer would result in the inaccurate operation cost of the prosumer.…”

Section: Introductionmentioning

confidence: 99%

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Day-Ahead Optimization of Prosumer Considering Battery Depreciation and Weather Prediction for Renewable Energy Sources

et al. 2020

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In recent years, taking advantage of renewable energy sources (RESs) has increased considerably due to their unique capabilities, such as a flexible nature and sustainable energy production. Prosumers, who are defined as proactive users of RESs and energy storage systems (ESSs), are deploying economic opportunities related to RESs in the electricity market. The prosumers are contracted to provide specific power for consumers in a neighborhood during daytime. This study presents optimal scheduling and operation of a prosumer owns RESs and two different types of ESSs, namely stationary battery (SB) and plugged-in electric vehicle (PHEV). Due to the intermittent nature of RESs and their dependency on weather conditions, this study introduces a weather prediction module in the energy management system (EMS) by the use of a feed-forward artificial neural network (FF-ANN). Linear regression results for predicted and real weather data have achieved 0.96, 0.988, and 0.230 for solar irradiance, temperature, and wind speed, respectively. Besides, this study considers the depreciation cost of ESSs in an objective function based on the depth of charge (DOD) reduction. To investigate the effectiveness of the proposed strategy, predicted output and the real power of RESs are deployed, and a mixed-integer linear programming (MILP) model is used to solve the presented day-ahead optimization problem. Based on the obtained results, the predicted output of RESs yields a desirable operation cost with a minor difference (US$0.031) compared to the operation cost of the system using real weather data, which shows the effectiveness of the proposed EMS in this study. Furthermore, optimum scheduling with regard to ESSs depreciation term has resulted in the reduction of operation cost of the prosumer and depreciation cost of ESS in the objective function has improved the daily operation cost of the prosumer by $0.8647.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Day-Ahead Optimization of Prosumer Considering Battery Depreciation and Weather Prediction for Renewable Energy Sources

et al. 2020

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“…Moreover, similar studies have often relied on traditional prediction techniques, which have potential limitations (eg, in terms of accuracy) in modelling complex behaviour, to predict/forecast building energy demand or PV energy generation. 29,30,[33][34][35][36]41 To overcome such limitations and improve prediction accuracy, we employ the DNN algorithm to forecast PV energy generation and EHP energy demand and subsequently determine the ESS charging/discharging schedules. [45][46][47][48][49][50] Another critical aspect of our study is related to the parameters used in forecasting EHP energy consumption and PV energy generation.…”

Section: Introductionmentioning

confidence: 99%

A novel deep learning‐based integrated photovoltaic, energy storage system and electric heat pump system: Optimising energy usage and costs

et al. 2021

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The use of photovoltaic (PV) systems has drawn attention as a solution to reduce the dependence on fossil fuel for building energy needs. Moreover, incorporating energy storage systems (ESSs) in PV systems can optimise electric energy costs by increasing dependency on PV-generated energy during electric peak load times. However, current ESSs have limited capacities making it difficult to fully maximise PV-generated energy. We propose a novel integrated energy-efficient system for PV, ESS and electric heat pump (EHP) that maximises the usage of PV energy, optimises ESS usage and reduces EHP energy consumption costs. The components of the proposed integrated system are linked with a deep learning (DL)-based algorithm that forecasts PV energy generation and energy demand of the EHP. The proposed system schedules the charging/discharging time of ESSs depending on peak load times, the forecasted EHP electric demand, and PV-generated energy. The data used were collected for 10 months from a retail shop equipped with an EHP and ESS. We found that the developed DL-based forecasting models for PV and EHP are accurate and reliable (ie, R 2 above 0.95). Also, the results show that the proposed integrated energy-efficient PV-ESS-EHP system saves 12% of the total annual electric costs, which corresponds to 1 285 291 Won. The proposed system ensures an efficient method to maximise PV-generated energy resulting in reduced dependency on fossil fuels for building energy needs.

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“…According to its system design, the system operator considers customer's load, generation from renewable energy resources, and participation in the electricity market such as demand response and ancillary services and other types of incentives [6,7]. To find the economically optimal strategy, control of charging and discharging the ESS is managed to earn maximum profit from savings of electricity tariff and incentives from the market [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

An Analysis of Battery Degradation in the Integrated Energy Storage System with Solar Photovoltaic Generation

Lee

Park

et al. 2020

Electronics

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Renewable energy generation and energy storage systems are considered key technologies for reducing greenhouse gas emissions. Energy system planning and operation requires more accurate forecasts of intermittent renewable energy resources that consider the impact of battery degradation on the system caused by the accumulation of charging and discharging cycles. In this study, a statistical model is presented for forecasting a day-ahead photovoltaic (PV) generation considering solar radiation and weather parameters. In addition, the technical performance of energy storage systems (ESS) should be evaluated by considering battery degradation that occurs during the charge and discharge cycles of the battery. In this study, a battery degradation model based on the data-driven method is used. Based on a suitable forecasting model, ESS scheduling is performed to charge the maximum amount of PV generation and discharge for the self-consumption of the customer load when PV generation ends. Since the battery is highly dependent on operating conditions such as depth of discharge, state of charge and temperature, two different ESS charge and discharge modes are proposed. From the simulation with the battery degradation model using parameters derived from experiments, we show that the battery is degraded along with charging cycles during testing periods. Variations in state of health are observed owing to the different characteristics of the battery according to the ESS operation modes, which are divided into the low and high SOC. Through experimental validation, it is proved that the state of charge (SOC), 0.45 is the optimal threshold that can determine the low and high SOC. Finally, the simulation results lead to the conclusion that the battery degradation in different operation modes should be taken into account to extend the end of life efficiently.

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

Cited by 8 publications

References 19 publications

Day-Ahead Optimization of Prosumer Considering Battery Depreciation and Weather Prediction for Renewable Energy Sources

Day-Ahead Optimization of Prosumer Considering Battery Depreciation and Weather Prediction for Renewable Energy Sources

A novel deep learning‐based integrated photovoltaic, energy storage system and electric heat pump system: Optimising energy usage and costs

An Analysis of Battery Degradation in the Integrated Energy Storage System with Solar Photovoltaic Generation

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