Sizing Renewable Generation and Energy Storage in Stand-Alone Microgrids Considering Distributionally Robust Shortfall Risk

Xie, Rui; Wei, Wei; Shahidehpour, Mohammad; Wu, Qiuwei; Mei, Shengwei

doi:10.1109/tpwrs.2022.3142006

Cited by 31 publications

(5 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(1e)-(1h) constitute the energy storage model, in which the charging and discharging power complementarity constraint p Sc i,t p Sd i,t = 0 is omitted. In fact, one can prove that this complementarity constraint is abundant in such an operation problem minimizing the operation cost, as Proposition 1 in [10] states. (1i) and (1j) contain the DC power flow model, where p L i j,t denotes the power flow in a transmission line from bus i to j in period t; θ N i,t is the nodal voltage angle; X L i j is the inductance parameter of one line; −S L i j ≤ p L i j,t ≤ S L i j contains the bounds for power flow in one line.…”

Section: Mathematical Model 21 Operation Optimizationmentioning

confidence: 98%

See 1 more Smart Citation

Distributionally Robust Renewable-Transmission-Storage Planning Considering Carbon Taxes

Xie

Wei

Mei

2022

2022 Asia Conference on Electrical, Power and Computer Engineering (EPCE 2022)

Self Cite

View full text Add to dashboard Cite

To protect the environment and lower carbon dioxide emissions, conventional fossil-fuel generators tend to be replaced by renewable generation. The volatile and uncertain renewable generation, as well as the reduced proportion of conventional fossil-fuel generators, causes challenges for power systems in terms of peak shaving. The equipment of energy storage is an important solution for the challenges. This paper proposes a distributionally robust method that coordinatedly plans renewable generation, transmission, and energy storage, minimizing the sum of investment and operation costs including carbon taxes. The operation cost is the optimal value of an operation optimization problem. Wasserstein-metric-based ambiguity set is established to deal with the possible inexactness of the empirical distribution of renewable generation and load uncertainties, based on which the worst-case expectation of operation cost is formulated. By calculating the Lipschitz constant of the operation cost function, the planning model comes down to mixedinteger linear programming. The case study in the IEEE 30-bus system demonstrates the effectiveness of the proposed method. CCS CONCEPTS• Hardware → Renewable energy; Batteries; Impact on the environment; Smart grid.

show abstract

Section: Mathematical Model 21 Operation Optimizationmentioning

confidence: 98%

“…Then by the property of 1-norm, L n (x) := max l L l,n (x) is a local Lipschitz constant of д(x, ξ ) as a function of ξ at point ξ n , cf. the proof of Proposition 2 in [10]. Let L(x) := max n ∈S N L n (x), then L(x) is the desired Lipschitz constant, which can be calculated by the following linear program.…”

Section: Transforming Worst-case Expectationmentioning

confidence: 99%

Distributionally Robust Renewable-Transmission-Storage Planning Considering Carbon Taxes

Xie

Wei

Mei

2022

2022 Asia Conference on Electrical, Power and Computer Engineering (EPCE 2022)

Self Cite

View full text Add to dashboard Cite

show abstract

“…Discrepancy-based models define the ambiguity sets as a ball within a certain measured space of uncertainty distributions [48,49]. Toward that end, Li et al [50] target microgrid operations and exploit the Wasserstein metric to construct the ambiguity set for uncertain renewable generation and load consumption.…”

Section: B Background and Related Workmentioning

confidence: 99%

“…We then dualize (48)(49)(50)(51)(52) to obtain the 'min' objective, where , , ν, and are the dual variables.…”

Section: B Tractable Mathematical Reformulationsmentioning

confidence: 99%

Blockchain-Based Water-Energy Transactive Management With Spatial-Temporal Uncertainties

Zhao

et al. 2023

IEEE Trans. Smart Grid

View full text Add to dashboard Cite

Water resources are vital to the energy conversion process but few efforts have been devoted to the joint optimization problem which is fundamentally critical to the water-energy nexus for small-scale or remote energy systems (e.g., energy hubs). Traditional water and energy trading mechanisms depend on centralized authorities and cannot preserve security and privacy effectively. Also, their transaction process cannot be verified and is subject to easy tampering and frequent exposures to cyberattacks, forgery, and network failures. Toward that end, water-energy hubs (WEHs) offers a promising way to analyse water-energy nexus for greater resource utilization efficiency.We propose a two-stage blockchain-based transactive management method for multiple, interconnected WEHs. Our method considers peer-topeer (P2P) trading and demand response, and leverages blockchain to create a secure trading environment. It features auditing and resource transaction record management via system aggregators enabled by a consortium blockchain, and entails spatial-temporal distributionally robust optimization (DRO) for renewable generation and load uncertainties. A spatial-temporal ambiguity set is incorporated in DRO to characterize the spatial-temporal dependencies of the uncertainties in distributed renewable generation and load demand. We conduct a simulation-based evaluation that includes robust optimization and the moment-based DRO as benchmarks. The results reveal that our method is consistently more effective than both benchmarks. Key findings include i) our method reduces conservativeness with lower WEH trading and operation costs, and achieves important performance improvements by up to 6.1%; and ii) our method is efficient and requires 18.7% less computational time than the moment-based DRO. Overall, this study contributes to the extant literature by proposing a novel two-stage blockchain-based WEH transaction method, developing a realistic spatialtemporal ambiguity set to effectively hedge against the uncertainties for distributed renewable generation and load demand, and producing empirical evidence suggesting its greater effectiveness and values than several prevalent methods., /Max & min buying power between WEHs.

show abstract

“…However, studies on DRO-based energy storage capacity allocation are still relatively few. Xie et al (2022) co-optimized the sizes of renewable generation and energy storage based on the DRO method in stand-alone microgrids, considering shortfall risk of load shedding, which minimizes the investment cost and the load shedding risk. Different with it, our work focuses on the balance between energy storage investment and renewable energy utilization capability.…”

Section: Introductionmentioning

confidence: 99%

Distributionally Robust Capacity Configuration for Energy Storage in Microgrid Considering Renewable Utilization

Ding

Zheng

et al. 2022

Front. Energy Res.

View full text Add to dashboard Cite

The energy storage plays an important role in the operation safety of the microgrid system. Appropriate capacity configuration of energy storage can improve the economy, safety, and renewable energy utilization of the microgrid. This study considers the uncertainty of renewable energy, and builds an energy storage capacity configuration (ESCC) in microgrid by using the distributionally robust optimization (DRO). This model co-optimizes energy storage planning, day-ahead scheduling, and renewable energy utilization of the microgrid, which derives the energy storage configuration strategy, balancing renewable energy utilization and operation economics of microgrid. The proposed model is a two-stage model with distributionally robust chance constrains. By applying decision rules, variable substitution, and duality techniques, this model is approximately transformed into a mixed integer programming problem with a second-order cone constraint, which can be directly solved. Experiments on the IEEE 33-bus system are carried out to verify the effectiveness and advantages of the proposed model.

show abstract

Sizing Renewable Generation and Energy Storage in Stand-Alone Microgrids Considering Distributionally Robust Shortfall Risk

Cited by 31 publications

References 44 publications

Distributionally Robust Renewable-Transmission-Storage Planning Considering Carbon Taxes

Distributionally Robust Renewable-Transmission-Storage Planning Considering Carbon Taxes

Blockchain-Based Water-Energy Transactive Management With Spatial-Temporal Uncertainties

Distributionally Robust Capacity Configuration for Energy Storage in Microgrid Considering Renewable Utilization

Contact Info

Product

Resources

About