Optimal planning and investment benefit analysis of shared energy storage for electricity retailers

Liu, Jichun; Chen, Xue; Xiang, Yue; Huo, Da; Liu, Junyong

doi:10.1016/j.ijepes.2020.106561

Cited by 48 publications

(14 citation statements)

References 29 publications

(27 reference statements)

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“…However, those works focused on maximizing the profit of the ES developer instead of the consumers. Another work that is mostly related to ours is [21] which studied the optimal planning and operation of an ES shared across multiple electricity retailers. The operation of the ES was centrally coordinated to minimize the total cost.…”

Section: A Related Workmentioning

confidence: 99%

“…In a nutshell, most existing works on shared ES models, especially CES models, have separately addressed the issues of ES planning (see [11,12]) and ES operation (see [14,15]). However, they are required to jointly considered to maximize the economic benefits [18,21]. Generally, the optimal sharing of the ES capitalizes on dynamical coordination of the users to explore their complementary features.…”

Section: A Related Workmentioning

confidence: 99%

“…However, this requires a paired cost allocation mechanism to distribute the co-created value among the users. Most existing works have applied heuristic cost allocation mechanisms (see [8,13,21]) which is intuitive and easy to implement but do not address the fairness, which is a critical to ensure the stable cooperation and preserve the co-created economic benefits. Some scarce works have discussed the fairness based on the core or Shapley value.…”

Section: A Related Workmentioning

confidence: 99%

“…Some scarce works have discussed the fairness based on the core or Shapley value. However, they either apply for some specific cases (e.g., price arbitrage [18]) or suffer from computational challenges [21].…”

Section: A Related Workmentioning

confidence: 99%

See 3 more Smart Citations

Optimal Sharing and Fair Cost Allocation of Community Energy Storage

Yong

Spanos

2020

Preprint

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This paper studies an ES sharing model where multiple buildings cooperatively invest and share a community ES (CES) to harness economic benefits from on-site renewable integration and utility price arbitrage. Particularly, we formulate the problem that integrates the optimal ES sizing, operation and cost allocation as a coalition game, which are generally addressed separately in the literature. Particularly, we address the fair expost cost allocation which hasn't been well studied. To overcome the computational challenge of computing the entire information of explicit characteristic functions (takes exponential time), we propose a fair cost allocation based on nucleolus by employing a constraints generation technique. We study the fairness and computational efficiency of the method through a number of case studies. The numeric results imply that the proposed method outperforms the Shapley approach and proportional method either in computational efficiency or fairness. Notably, for the proposed method, only a small fraction of characteristic functions (2.54%) is computed to achieve the cost allocation versus the entire information required by Shapley approach. With the proposed cost allocation, we investigate the enhanced economic benefits of the CES model over individual ES (IES) installation. We see the CES model provides higher cost reduction to each committed buildings. Moreover, the value of storage is obviously improved (about 1.83 times) with the CES model over the IES model.

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Section: A Related Workmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Optimal Sharing and Fair Cost Allocation of Community Energy Storage

Yong

Spanos

2020

Preprint

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“…[8] adopted genetic algorithm to study the distributed energy storage charging/discharging strategy with minimum line loss in the distribution network. [9] adopted an energy storage charging and discharging strategy to track load and electricity price fluctuations to smooth load fluctuations. [10] analyzed the influence of the operation mode of user-side distributed energy storage on the network loss of the distribution network, but did not study specific charging and discharging strategies.…”

Section: Introductionmentioning

confidence: 99%

Distribution Network Dispatching Optimization Strategy Based on Time-of-use Electricity Price and User-Side Energy Storage

Wang

2021

Lecture Notes in Electrical Engineering

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This paper studies the participation of user-side energy storage in the optimized operation of the distribution network, establishes a user load response model based on the time-of-use electricity price, and builds a distribution network operation optimization model with the goal of minimizing the user's daily electricity cost and the distribution network loss. The evaluation function method is used to convert multiple goals into single goals, and the genetic simulated annealing algorithm is used to solve the optimal user-side energy storage charging and discharging strategy. Simulation verification is carried out based on the IEEE-33 node power distribution system. The results prove the effectiveness of the strategy proposed in this paper. And the superiority of the proposed algorithm.

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Deep reinforcement learning for resilient microgrid expansion planning with multiple energy resource

Pang

Zhou

Tsianikas

et al. 2022

Quality & Reliability Eng

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Microgrid has attracted more and more attention to provide backup power for customers in the case of power grid outages. Microgrid expansion planning is significant to handle the increasing customer demand and to enhance power resilience. Current research about long‐term microgrid expansion planning rarely if ever considered the uncertainties associated with energy storage and power generation units, for example, battery cycle degradation. These factors have important influence on the performance of microgrid expansion planning in reality. In this paper, a long‐term microgrid expansion planning model with multiple energy resource is presented. Deep reinforcement learning method is used to obtain the cost‐effective microgrid expansion policies to enhance power resilience. In the case study, optimal microgrid expansion planning is achieved based on the proposed model. The impacts of battery degradation and resilience constraint on microgrid expansion policy optimization are also investigated. The simulation results prove the effectiveness of the proposed method on economic and resilient microgrid expansion planning.

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Optimal planning and investment benefit analysis of shared energy storage for electricity retailers

Cited by 48 publications

References 29 publications

Optimal Sharing and Fair Cost Allocation of Community Energy Storage

Optimal Sharing and Fair Cost Allocation of Community Energy Storage

Distribution Network Dispatching Optimization Strategy Based on Time-of-use Electricity Price and User-Side Energy Storage

Deep reinforcement learning for resilient microgrid expansion planning with multiple energy resource

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