Benefits of Home Energy Storage Utilization: An Australian Case Study of Demand Charge Practices in Residential Sector

Kong, Weicong; Luo, Fengji; Jia, Youwei; Zhang, Dong; Liu, Junwei

doi:10.1109/tsg.2021.3054126

Cited by 25 publications

(12 citation statements)

References 13 publications

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“…Moreover, an adequate real-time control should be proposed to consider forecasting uncertainties and ESS operating conditions. A dynamic programming-based scheduling control scheme for residential ESS is proposed in [26]. It determines the optimal charging/discharging of the ESS to minimise the energy cost for the residential customers based on the real use case of Australian network tariffs.…”

Section: A Related Workmentioning

confidence: 99%

Design and Field Implementation of a Hierarchical Control Solution for Residential Energy Storage Systems

Bahloul

Breathnach²,

Khadem

2023

IEEE Trans. Smart Grid

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Section: A Related Workmentioning

confidence: 99%

Design and Field Implementation of a Hierarchical Control Solution for Residential Energy Storage Systems

Bahloul

Breathnach²,

Khadem

2023

IEEE Trans. Smart Grid

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“…However, the afore-mentioned works in the literature only consider the benefits of the demand side while ignoring the benefits of the supply side. In the electricity market, EVA (Okur et al, 2020;Kong et al, 2021) plays an important role in integrating demand, participating in bidding, and purchasing resources. Qiu et al (2020) and Tao et al (2022) studied the efficient pricing problem from the perspective of EVA.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimal scheduling of charging stations based on deep reinforcement learning

Cui

Lin²,

Zhang³

et al. 2023

Front. Energy Res.

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With the green-oriented transition of energy, electric vehicles (EVs) are being developed rapidly to replace fuel vehicles. In the face of large-scale EV access to the grid, real-time and effective charging management has become a key problem. Considering the charging characteristics of different EVs, we propose a real-time scheduling framework for charging stations with an electric vehicle aggregator (EVA) as the decision-making body. However, with multiple optimization objectives, it is challenging to formulate a real-time strategy to ensure each participant’s interests. Moreover, the uncertainty of renewable energy generation and user demand makes it difficult to establish the optimization model. In this paper, we model charging scheduling as a Markov decision process (MDP) based on deep reinforcement learning (DRL) to avoid the afore-mentioned problems. With a continuous action space, the MDP model is solved by the twin delayed deep deterministic policy gradient algorithm (TD3). While ensuring the maximum benefit of the EVA, we also ensure minimal fluctuation in the microgrid exchange power. To verify the effectiveness of the proposed method, we set up two comparative experiments, using the disorder charging method and deep deterministic policy gradient (DDPG) method, respectively. The results show that the strategy obtained by TD3 is optimal, which can reduce power purchase cost by 10.9% and reduce power fluctuations by 69.4%.

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“…The suppliers and aggregators have many advantages, such as new offerings for consumers/prosumers and DERs (such as EV, ES, microgeneration systems, flexible loads, HP etc.) suppliers, mitigating commercial risk, and proposing/extending new business opportunities [21].…”

Section: Introductionmentioning

confidence: 99%

Role of Aggregator in Coordinating Residential Virtual Power Plant in “StoreNet” : A Pilot Project Case Study

Bahloul

Breathnach

Cotter³

et al. 2022

IEEE Trans. Sustain. Energy

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Towards the development and demonstration of an innovative business model where the value proposition for consumers/prosumers, aggregators and network operators are well maintained, this study assesses the performance of different aggregation control strategies for a distributed energy storage based residential virtual power plant (VPP). A special focus is given on the social welfare and network strength and their relation to energy storage capacity and power budgets allocation. The study is based on a real-life demonstration project, StoreNet where the basic self-consumption (SB-SC) control strategy has already been deployed. Analysing one-year measured data, it is observed that the implemented SB-SC approach allows 16%-19% electricity cost-saving, whereas the proposed VPP-bill minimisation approach can benefit from 37%-42% cost saving. This is also 7%-8% higher than the single home bill minimisation approach where the community does not participate in the VPP model. In contrast, the peak shaving approach is more favourable for the network operator. It reduces the load peak by 46.5%-64.7% and also drastically reduces the benefits for the customers and aggregator. Based on these studies and learning, some recommendations are made addressing the integration aspect of residential VPP and the future development of this concept for the local and wholesale energy markets.

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Benefits of Home Energy Storage Utilization: An Australian Case Study of Demand Charge Practices in Residential Sector

Cited by 25 publications

References 13 publications

Design and Field Implementation of a Hierarchical Control Solution for Residential Energy Storage Systems

Design and Field Implementation of a Hierarchical Control Solution for Residential Energy Storage Systems

Multi-objective optimal scheduling of charging stations based on deep reinforcement learning

Role of Aggregator in Coordinating Residential Virtual Power Plant in “StoreNet” : A Pilot Project Case Study

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