Demand Side Management and the Participation in Consecutive Energy Markets - A Multistage Stochastic Optimization Approach

A Demand-side management technique are deployed along with battery energy-storage systems (BESSs) to lower the electricity cost by mitigating the peak load of a building. Most of the existing methods rely on manual operation of the BESS, or even an elaborate building energy-management system resorting to a deterministic method that is susceptible to unforeseen growth in demand. In this study we propose a real-time optimal operating strategy for BESS based on density demand forecast and stochastic optimization. This method takes into consideration uncertainties in demand when accounting for an optimal BESS schedule, making it robust compared to the deterministic case. The proposed method is verified and tested against existing algorithms. Data obtained from a real site in South Korea is used for verification and testing. The results show that the proposed method is effective, even for the cases where the forecasted demand deviates from the observed demand

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Two-time-scale stochastic optimization dispatch of PV-microgrid considering demand side management

2017 36th Chinese Control Conference (CCC) ◽

10.23919/chicc.2017.8029045 ◽

2017 ◽

Author(s):

Shilin Liu ◽

Weiwei Yu

Keyword(s):

Stochastic Optimization ◽

Time Scale ◽

Demand Side Management ◽

Demand Side

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A multi-stage stochastic optimization approach to optimal bidding on energy markets

IEEE Conference on Decision and Control and European Control Conference ◽

10.1109/cdc.2011.6161169 ◽

2011 ◽

Cited By ~ 4

Author(s):

Laura Puglia ◽

Daniele Bernardini ◽

Alberto Bemporad

Keyword(s):

Stochastic Optimization ◽

Energy Markets ◽

Optimization Approach ◽

Multi Stage ◽

Optimal Bidding

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Online Demand Side Management with PEVs Using Stochastic Optimization

10.20944/preprints201806.0254.v1 ◽

2018 ◽

Author(s):

Moses Acquah ◽

Byeonggu Yu ◽

Sekyung Han

Keyword(s):

Stochastic Optimization ◽

Electric Vehicles ◽

Distributed Storage ◽

Storage System ◽

Demand Side Management ◽

Demand Side ◽

Demand Forecast ◽

Potential Growth ◽

Battery Energy

This study purposes the use of plug-in electric vehicles for demand side management (DSM) considering uncertainties in demand as well as uncertainties due to mobility of PEV to mitigate peak demand. The solution also seeks to reduce electric cost in addition to reducing the effects of greenhouse gases. In recent years DSM using distributed storage system such as battery energy management system (BESS) and plugged-in electric vehicles (PEV) have become very prevalent with most implementations resorting to deterministic load forecast. These methods do not consider the potential growth in demand making their solutions less robust. In this study we propose a real-time density demand forecast and stochastic optimization for robust operation of PEV for a building. This method accounts for demand uncertainties in addition to uncertainties in mobile energy storage as found in PEV, making the resulting solution robust as compared to the deterministic case. A case study on a real site in South Korea is used for verification and testing. The proposed study is verified and tested against existing algorithms. The result verifies the effectiveness of the proposed approach

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