Two-stage robust planning-operation co-optimization of energy hub considering precise energy storage economic model

Chen, Cong; Sun, Hongbin; Shen, Xinwei; Guo, Ye; Guo, Qinglai; Xia, Tian

doi:10.1016/j.apenergy.2019.113372

Cited by 117 publications

(33 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The design of sustainable SEHS in order to obtaining optimal size of resources with consideration RESs uncertainty using Benders decomposition algorithm (BDA) is studied in [20]. In [21] multi-period operation optimization of the energy hybrid systems by Particle Swarm Optimization (PSO) algorithm and interior-point approach (IPA) is analysed. The co-optimization modelling of the energy hybrid system as two-stage robust planning-operation and with attention to lifetime of the electrical storage systems (ESSs) is studied in [22].…”

Section: Related Workmentioning

confidence: 99%

Multi-objectives Optimal Scheduling in Smart Energy Hub System with Electrical and Thermal Responsive Loads

Chamandoust

Derakhshan

Hakimi

et al. 2020

Environmental and Climate Technologies

View full text Add to dashboard Cite

AbstractIn this study, multi-objective optimal scheduling of smart energy Hub system (SEHS) in the day ahead is proposed. A SEHS is comprising of interconnected energy hybrid system infrastructures such as electrical, thermal, wind, solar, natural gas and other fuels to supply many types of electrical and thermal loads in a two-way communication platform. All objectives in this paper, are minimized and consist of 1) operation cost and emission polluting in generation side, 2) loss of energy supply probability (LESP) in demand side, and 3) deviation of electrical and thermal loads with the optimal level of electrical and thermal profile in the day ahead. The third objective to flatten electrical and thermal demand profile using Demand Side Management (DSM) by the optimal shifting of electrical and thermal shiftable loads (SLs) is proposed. Also, stochastic modelling of renewable energy sources (RESs) and electrical and thermal loads by Monte Carlo technique is modelled. Using GAMS optimization software, proposed approach by ε -constraint method for obtaining to non-dominated Pareto solutions of objectives is implemented. Moreover, by the decision-making method, the best solution of non-dominated Pareto solutions is selected. Finally, two case studies and sensitivity analysis in case studies for confirmation of the proposed approach are analysed.

show abstract

Section: Related Workmentioning

confidence: 99%

Multi-objectives Optimal Scheduling in Smart Energy Hub System with Electrical and Thermal Responsive Loads

Chamandoust

Derakhshan

Hakimi

et al. 2020

Environmental and Climate Technologies

View full text Add to dashboard Cite

show abstract

“…Constraint (14) defines the maximum charging and discharging power; constraint (15) restricts the energy stored in the battery; constraint (16) denotes the relationship between the energy stored and the exchanged power. 4) Energy balance constraints…”

Section: Aggregation Model Formulationmentioning

confidence: 99%

“…Like [14], the proposed model is formulated as a twostage RO problem. Dual norm [16], Benders decomposition [17], and column-and-constraint generation (C&CG) algorithm [18] are the most commonly used algorithms to solve the two-stage RO problem. The C&CG algorithm can achieve better performance in most cases [18], [19].…”

Section: Introductionmentioning

confidence: 99%

Active Dynamic Aggregation Model for Distributed Integrated Energy System as Virtual Power Plant

Zhao

Wang

et al. 2020

Journal of Modern Power Systems and Clean Energy

Self Cite

View full text Add to dashboard Cite

Distributed integrated multi-energy systems (DIMSs) can be regarded as virtual power plants to provide additional flexibility to the power system. This paper proposes a robust active dynamic aggregation model for the DIMSs to describe the maximum feasible region. The aggregation model includes the power constraints, energy constraints, and ramping constraints to aggregate different types of resources in the DIMSs. The proposed generator-like and storage-like model does not depend on the ancillary service market and can be directly incorporated into the economic dispatch model of the power system. A novel algorithm based on the column-and-constraint generation algorithm and convex-concave procedure is proposed to solve the two-stage robust optimization problem, which is more efficient than the KKT-based algorithms. Finally, a case study of an actual DIMS is developed to demonstrate the effectiveness of the proposed model. Index Terms-Aggregation, distributed integrated energy system (DIMS), robust optimization, virtual power plant. NOMENCLATURE A. Sets and Parameters ϒ Ω ε CCP ε η EBi η ci η dci Set of economic dispatch periods Uncertainty set Convergence tolerance of the convex-concave procedure (CCP) Convergence tolerance of upper power value UB Efficiency of electric boiler i Charging and discharging efficiency of battery i c pmin t

show abstract

“…Extensive efforts have been made to study the optimal planning of the ESS. Uncertainties [7], reliability [6] and life of ESS [8] have been taken into consideration during the ESS capacity configuration. However, the impact of the demand response (DR) of the EC on the ESS planning scheme has not been fully investigated.…”

Section: Introductionmentioning

confidence: 99%

A Planning Method for Energy Storage System of Integrated Community Energy System Considering Demand Response

Li¹,

Hua²,

Jiang³

et al. 2021

E3S Web Conf.

View full text Add to dashboard Cite

Demand response plays a significant role in peak load shifting, storage capacity configuration and renewable energy utilization. A bi-level planning method for energy storage system of integrated community energy system considering the demand response is proposed in this paper. In the upper level, the investment cost of electrical energy storage and thermal energy storage, operation and maintenance cost and fuel cost of the integrated community energy system, as well as the compensation cost to the energy consumer, are considered; in the lower level, the responded demand of the energy consumer is taken into consideration to minimize the energy bill of the energy consumer. An actual planning for energy storage system of integrated community energy system shows the effectiveness of the proposed method.

show abstract

Two-stage robust planning-operation co-optimization of energy hub considering precise energy storage economic model

Cited by 117 publications

References 31 publications

Multi-objectives Optimal Scheduling in Smart Energy Hub System with Electrical and Thermal Responsive Loads

Multi-objectives Optimal Scheduling in Smart Energy Hub System with Electrical and Thermal Responsive Loads

Active Dynamic Aggregation Model for Distributed Integrated Energy System as Virtual Power Plant

A Planning Method for Energy Storage System of Integrated Community Energy System Considering Demand Response

Contact Info

Product

Resources

About