scholarly journals Optimal ESS Scheduling for Peak Shaving of Building Energy Using Accuracy-Enhanced Load Forecast

Energies ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 5633
Author(s):  
Jin Sol Hwang ◽  
Ismi Rosyiana Fitri ◽  
Jung-Su Kim ◽  
Hwachang Song
Keyword(s):  
Deep Learning ◽  
Optimization Problem ◽  
Scheduling Algorithm ◽  
Storage System ◽  
Peak Load ◽  
Energy Storage System ◽  
Building Energy ◽  
Load Forecast ◽  
Energy Prediction ◽  
Real Building

This paper proposes an optimal Energy Storage System (ESS) scheduling algorithm Building Energy Management System (BEMS). In particular, the focus is placed on how to reduce the peak load using ESS and load forecast. To this end, first, an existing deep learning-based load forecast method is applied to a real building energy prediction and it is shown that the deep learning-based method leads to an accuracy-enhanced load forecast. Second, an optimization problem is formulated in order to devise an ESS scheduling. In the optimization problem, the objective function and constraints are defined such that the peak load is reduced; the cost for electricity is minimized; and the ESS’s lifetime is elongated considering the accuracy-enhanced load forecast, real-time electricity price, and the state-of-charge of the ESS. For the purpose of demonstrating the effectiveness of the proposed ESS scheduling method, it is implemented using a real building load power and temperature data. The simulation results show that the proposed method can reduce the peak load and results in smooth charging and discharging, which is important for the ESS lifetime.

scholarly journals Adequacy Assessment of Wind Integrated Generating Systems Incorporating Demand Response and Battery Energy Storage System

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2649 ◽  
Author(s):  
Jiashen Teh
Keyword(s):  
Energy Storage ◽  
Demand Response ◽  
Renewable Energy Sources ◽  
Storage System ◽  
Peak Load ◽  
Energy Storage System ◽  
Battery Energy Storage System ◽  
Battery Energy Storage ◽  
Battery Energy ◽  
Generating System

The demand response and battery energy storage system (BESS) will play a key role in the future of low carbon networks, coupled with new developments of battery technology driven mainly by the integration of renewable energy sources. However, studies that investigate the impacts of BESS and its demand response on the adequacy of a power supply are lacking. Thus, a need exists to address this important gap. Hence, this paper investigates the adequacy of a generating system that is highly integrated with wind power in meeting load demand. In adequacy studies, the impacts of demand response and battery energy storage system are considered. The demand response program is applied using the peak clipping and valley filling techniques at various percentages of the peak load. Three practical strategies of the BESS operation model are described in this paper, and all their impacts on the adequacy of the generating system are evaluated. The reliability impacts of various wind penetration levels on the generating system are also explored. Finally, different charging and discharging rates and capacities of the BESS are considered when evaluating their impacts on the adequacy of the generating system.

Investigation and evaluation of building energy flexibility with energy storage system in hot summer and cold winter zones

2022 ◽  
Vol 46 ◽  
pp. 103877
Author(s):  
Jiangyang Liu ◽  
Xu Yang ◽  
Zhongbing Liu ◽  
Juan Zou ◽  
Yaling Wu ◽  
...  
Keyword(s):  
Energy Storage ◽  
Storage System ◽  
Energy Storage System ◽  
Building Energy ◽  
Cold Winter

Deep learning-based feature engineering methods for improved building energy prediction

2019 ◽  
Vol 240 ◽  
pp. 35-45 ◽  
Author(s):  
Cheng Fan ◽  
Yongjun Sun ◽  
Yang Zhao ◽  
Mengjie Song ◽  
Jiayuan Wang
Keyword(s):  
Deep Learning ◽  
Building Energy ◽  
Feature Engineering ◽  
Energy Prediction

scholarly journals Practical Operation Strategies for Energy Storage System under Uncertainty

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 1098 ◽  
Author(s):  
Minsoo Kim ◽  
Kangsan Kim ◽  
Hyungeun Choi ◽  
Seonjeong Lee ◽  
Hongseok Kim
Keyword(s):  
Real Time ◽  
Peak Power ◽  
Storage System ◽  
Peak Load ◽  
Load Forecasting ◽  
Energy Storage System ◽  
Real Time Control ◽  
Time Control ◽  
Forecasting Error ◽  
Marginal Power

Recent advances in battery technologies have reduced the financial burden of using the energy storage system (ESS) for customers. Peak cut, one of the benefits of using ESS, can be achieved through proper charging/discharging scheduling of ESS. However, peak cut is sensitive to load-forecasting error, and even a small forecasting error may result in the failure of peak cut. In this paper, we propose a two-phase approach of day-ahead optimization and real-time control for minimizing the total cost that comes from time-of-use (TOU), peak load, and battery degradation. In day-ahead optimization, we propose to use an internalized pricing to manage peak load in addition to the cost from TOU. The proposed method can be implemented by using dynamic programming, which also has an advantage of accommodating the state-dependent battery degradation cost. Then in real-time control, we propose a concept of marginal power to alleviate the performance loss incurred from load-forecasting error and mimic the offline optimal battery scheduling by learning from load-forecasting error. By exploiting the marginal power, real-time ESS charging/discharging power gets close to the offline optimal battery scheduling. Case studies show that under load-forecasting uncertainty, the peak power using the proposed method is only 22.4% higher than the offline optimal peak power, while the day-ahead optimization has 76.8% higher peak power than the offline optimal power. In terms of profit, the proposed method achieves 77.0% of the offline optimal profit while the day-ahead method only earns 19.6% of the offline optimal profit, which shows the substantial improvement of the proposed method.

scholarly journals A New Power Sharing Scheme of Multiple Microgrids and an Iterative Pairing-Based Scheduling Method

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1605 ◽  
Author(s):  
Hong-Chao Gao ◽  
Joon-Ho Choi ◽  
Sang-Yun Yun ◽  
Seon-Ju Ahn
Keyword(s):  
Scheduling Algorithm ◽  
Storage System ◽  
Operating Cost ◽  
Optimization Method ◽  
Energy Storage System ◽  
Power Sharing ◽  
Mixed Integer ◽  
Total System ◽  
Peak Demand ◽  
Sharing Scheme

As the numbers of microgrids (MGs) and prosumers are increasing, many research efforts are proposing various power sharing schemes for multiple MGs (MMGs). Power sharing between MMGs can reduce the investment and operating costs of MGs. However, since MGs exchange power through distribution lines, this may have an adverse effect on the utility, such as an increase in peak demand, and cause local overcurrent issues. Therefore, this paper proposes a power sharing scheme that is beneficial to both MGs and the utility. This research assumes that in an MG, the energy storage system (ESS) is the major controllable resource. In the proposed power sharing scheme, an MG that sends power should discharge at least as much power from the ESS as the power it sends to other MGs, in order to actually decrease the total system demand. With these assumptions, methods for determining the power sharing schedule are proposed. Firstly, a mixed integer linear programming (MILP)-based centralized approach is proposed. Although this can provide the optimal power sharing solution, in practice, this method is very difficult to apply, due to the large calculation burden. To overcome the significant calculation burden of the centralized optimization method, a new method for determining the power sharing schedule is proposed. In this approach, the amount of power sharing is assumed to be a multiple of a unit amount, and the final power sharing schedule is determined by iteratively finding the best MG pair that exchange this unit amount. Simulation with a five MG scenario is used to test the proposed power sharing scheme and the scheduling algorithm in terms of a reduction in the operating cost of MGs, the peak demand of utility, and the calculation burden. In addition, the interrelationship between power sharing and the system loss is analyzed when MGs exchange power through the utility network.

scholarly journals Battery Life Enhancement in a Hybrid Electrical Energy Storage System Using a Multi-Source Inverter

2019 ◽  
Vol 10 (2) ◽  
pp. 17 ◽  
Author(s):  
Yogesh Mahadik ◽  
K. Vadirajacharya
Keyword(s):  
Electric Vehicles ◽  
Storage System ◽  
Peak Load ◽  
Electrical Energy ◽  
Energy Storage System ◽  
Open Loop ◽  
Battery Life ◽  
Loop Control ◽  
Power Exchange ◽  
Driving Range

This paper introduces a new topology using a multi-source inverter with the intention of reducing the battery current and weight, while enhancing the battery life and increasing the driving range for plug-in electric vehicles, with the combination of a battery and an ultracapacitor (UC) as storage devices. The proposed topology interconnects the UC and battery directly to the three-phase load with a single-stage conversion using an inverter. The battery life is considerably reduced due to excess (peak) current drawn by the load, and these peak load current requirements are met by connecting the ultracapacitor to the battery, controlled through an inverter. Here, the battery is used to cater to the needs of constant profile energy demands, and the UC is used to meet the dynamic peak load profile. This system is highly efficient and cost-effective when compared to a contemporary system with a single power source. Through a comparative analysis, the cost-effectiveness of the proposed energy management system (EMS) is explained in this paper. Energy and power exchange are implemented with an open-loop control strategy using the PSIM simulation environment, and the system is developed with a hardware prototype using different modes of inverter control, which reduces the average battery current to 27% compared to the conventional case. The driving range of electric vehicles is extended using active power exchange between load and the sources. The dynamics of the ultracapacitor gives a quick response, with battery current shared by the ultracapacitor. As a result, the battery current is reduced, thereby enhancing the driving cycle. With the prototype, the results of the proposed topology are validated.

An Agent Based Simulation for Building Energy System Modeling

2010 ◽  
Author(s):  
Mengqi Hu ◽  
Jin Wen ◽  
Fan Li ◽  
Moeed Haghnevis ◽  
Yasaman Khodadadegan ◽  
...  
Keyword(s):  
System Modeling ◽  
Storage System ◽  
Energy System ◽  
Energy Storage System ◽  
Building Energy ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Time Price ◽  
Building Energy System ◽  
Energy System Modeling

Extensive research has been done on the centralized building energy system modeling and simulation. However the centralized structure is limited to study and simulate the energy interaction between different buildings at different locations. This paper reviews the building energy consumption model, energy storage system and energy generation system in the Net-zero buildings. Incorporate with the real-time price rate model, this paper develops an agent based simulation framework for distributed building energy system under uncertainty. Each sub system is developed as an agent in the simulation model, and a virtual decision agent is designed to simulate the operation strategy. The energy flow between different agents can be easily monitored from the simulation. The differences between on-peak and off-peak control are demonstrated from the simulation result.

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