Strategic Bidding of Hybrid AC/DC Microgrid Embedded Energy Hubs: A Two-Stage Chance Constrained Stochastic Programming Approach

Zhao, Tianyang; Pan, Xuewei; Yao, Shuhan; Ju, Chenchen; Li, Lei

doi:10.1109/tste.2018.2884997

Cited by 80 publications

(40 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This model supplies the demands by using a CHP unit, gas boiler, thermal storage, solar system and importing electricity from the network and cannot export electricity to the network. A twostage stochastic method is proposed to model the uncertain parameters in [12]. The uncertainties of the proposed energy hub model are price, electrical demands and ambient temperature.…”

Section: B Literature Reviewmentioning

confidence: 99%

Short-Term Self-Scheduling of Virtual Energy Hub Plant Within Thermal Energy Market

Jadidbonab

Mohammadi‐Ivatloo

Marzband

et al. 2021

IEEE Trans. Ind. Electron.

121

View full text Add to dashboard Cite

Section: B Literature Reviewmentioning

confidence: 99%

Short-Term Self-Scheduling of Virtual Energy Hub Plant Within Thermal Energy Market

Jadidbonab

Mohammadi‐Ivatloo

Marzband

et al. 2021

IEEE Trans. Ind. Electron.

121

View full text Add to dashboard Cite

“…Constraints (26) and (30) present the limits for demand deviation under DR program. The new load profile by applying DR programs are limited under the maximum value in (25) and (29). The total daily electricity and heat demand should be unchanged, which are presented in (27) and (31).…”

Section: Coupling Relationshipmentioning

confidence: 99%

“…Compared with RO, SO is another extreme as it assumes the explicit uncertainty distributions. [29][30][31] Notwithstanding, the solutions are more accurate based on a large number of sample sets but it causes low computational efficiency. Moreover, it is always challenging to obtain sufficient historical data to model real distributions.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust energy hub optimization with cross‐vector demand response

Zhu

Zhao

et al. 2020

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

The integration of multi-energy systems increases renewable penetration and efficiency of energy use, and reduces costs. This integration is further reinforced by new technologies, such as cross-vector demand response and power-togas technologies, bringing big flexibility to system operation. This paper studies the optimal operation of multi-vector energy systems, considering cross-vector demand response with power-togas technology by using robust optimization. An energy hub system (EHS) is considered as the realization of a multi-vector energy system, which consists of (a) the two-way multienergy converters between electricity and gas, (b) an energy storage system and (c) renewable energy resources. The demand response to energy price variations in both electricity and heat is considered, where customers can change their energy consumption behaviors motivated by pricing mechanisms. In the EHS, energy conversion, storage, production and consumption are all modeled. To handle the uncertainty from renewable power generation, robust optimization is used with a hybrid box-polyhedral uncertainty set for solving the built model. Case studies demonstrate that the proposed method can

show abstract

“…For instance, the electrical storage saves energy at low price intervals and discharge it in peak hours [3]. A stochastic day-ahead bidding strategy for energy hub is proposed in [4], considering the presence of various energy storages including battery energy storage, heating storage, and ice storage while neglecting the cooperation of various hubs and demand response programs. A networked energy hub system is proposed in [5], which aims to maximize the profit of the system by using the alternating direction method of multipliers (ADMM) method.…”

Section: Introductionmentioning

confidence: 99%

Cooperative energy management of multi-energy hub systems considering demand response programs and ice storage

Bahmani

Karimi

Jadid

2021

International Journal of Electrical Power & Energy Systems

View full text Add to dashboard Cite

Energy hub systems integrate various energy sources and interconnect different energy carriers in order to enhance the flexibility of the system. In this paper, a cooperative framework is proposed in which a network of energy hubs collaborate together and share their resources in order to reduce their costs. Each hub has several sources including CHP, boiler, renewable sources, electrical chiller, and absorption chiller. Moreover, energy storages are considered for electrical, heating, and cooling systems in order to increase the flexibility of energy hubs. Unlike the methods based on Nash-equilibrium points, which find the equilibrium point and have no guarantee for optimality of the solution, the employed cooperative method finds the optimal solution for the problem. We utilize the Shapley value to allocate the overall gain of the hub's coalition based on the contribution and efficiency of the energy hubs. The proposed method is modeled as a mixed integer linear programming problem, and the cost of network energy hubs are decreased in the cooperative operation, which shows the efficiency of this model. The results show 18.89, 10.23, and 8.72% improvement for hub1, hub2, and hub3, respectively, by using the fair revenue mechanism.

show abstract

Strategic Bidding of Hybrid AC/DC Microgrid Embedded Energy Hubs: A Two-Stage Chance Constrained Stochastic Programming Approach

Cited by 80 publications

References 32 publications

Short-Term Self-Scheduling of Virtual Energy Hub Plant Within Thermal Energy Market

Short-Term Self-Scheduling of Virtual Energy Hub Plant Within Thermal Energy Market

Robust energy hub optimization with cross‐vector demand response

Cooperative energy management of multi-energy hub systems considering demand response programs and ice storage

Contact Info

Product

Resources

About