Security-Constrained Multiperiod Economic Dispatch With Renewable Energy Utilizing Distributionally Robust Optimization

Lu, Xi; Chan, Ka Wing; Xia, Shiwei; Zhou, Bin; Luo, Xiao

doi:10.1109/tste.2018.2847419

Cited by 77 publications

(57 citation statements)

References 40 publications

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“…In (16), the reactive power of PV is described by the forecasted active PV power output and the power factor as defined in (17). The reactive power from capacitor banks is given in (18). The linearized DistFlow for distribution systems is presented in (19) and (20).…”

Section: System Modellingmentioning

confidence: 99%

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Reactive Power Optimization in Integrated Electricity and Gas Systems

Zhao

Xiang

et al. 2021

IEEE Systems Journal

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Volt/VAR optimization (VVO) is one important operation in distribution systems to maintain acceptable voltage profiles. However, the high penetration of renewable generation poses severe challenges to VVO, leading to voltage deviation and fluctuation. This is further complicated by the growing coupling between electricity and natural gas systems. To resolve unacceptable voltage deviation under energy system interdependency, this paper proposes a co-optimization of VVO for an integrated electricity and gas system (IEGS) with uncertain renewable generation. A two-stage data-driven distributionally robust optimization (DRO) is developed to model the coordinated optimization problem, which determines two-stage VVO and operation schemes with dispatch and corrective adjustment through active power regulation and reactive power support in both dayahead and real-time stage. A semidefinite programming is reformulated to ensure the tractability and the proposed problem is solved by a constraint generation framework. Simulation studies are conducted on a 33-bus-6-node IEGS. Case studies demonstrate that the interdependency between electricity and gas systems reduces siginificant operation cost and voltage rise. It thus can benefit integrated system operators with a powerful operation tool to manage systems with fewer costs but integrate more renewable energy while maintaining high supply quality.

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Section: System Modellingmentioning

confidence: 99%

“…A security-constrained two-stage ED with renewable power uncertainty modelled by DRO is designed in [18]. The segregated linear decision rule is used to affinely approximate the decision variables after the first time period which reduces the computational burden.…”

Section: Introductionmentioning

confidence: 99%

Reactive Power Optimization in Integrated Electricity and Gas Systems

Zhao

Xiang

et al. 2021

IEEE Systems Journal

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“…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%

“…RO makes decisions based on the worst-case scenario, which effectively improves the optimization reliability. 24,25 Paper 26 proposes a dispatch model of large-scale hybrid wind/PV/hydro/thermal power system with RO framework. The intermittent power supply is modeled based on the flexible control of robustness.…”

Section: Introductionmentioning

confidence: 99%

Robust energy hub optimization with cross‐vector demand response

Zhu

Zhao

et al. 2020

Int Trans Electr Energ Syst

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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

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“…Nevertheless, the intermittent and stochastic natures of distributed RESs have introduced great challenges for the efficiency and security of energy distribution system [4,5]. The peakregulating pressure of thermal power plant (TPP) is also sharply increasing [6,7], leading to the rising of the operational cost for thermal power unit (TPU). As a result, a larger number of TPPs are hard to survive.…”

mentioning

confidence: 99%