Adaptive robust operation of the active distribution network including renewable and flexible sources

Kiani, Hossein; Hesami, Kamal; Azarhooshang, Alireza; Pirouzi, Sasan; Safaee, Sheila

doi:10.1016/j.segan.2021.100476

Cited by 42 publications

(79 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, the point corresponding to the best compromise solution among the mentioned functions is equal to the maximum value of  for all values selected for weight coefficients [24]. Equations ( 2)-( 12) represent the constraints of the upperlevel problem, where (2)-( 6) refer to the LAC-PF constraints in the SDN [1,7]. These constraints indicate active and reactive power balance at each bus, active and reactive power flow through the distribution line, and voltage angle of the slack bus.…”

Section: Iimodel Of Proposed Problemmentioning

confidence: 99%

“…The problem is subject to operation constraints of the network and charging and discharging constraints of EVs batteries and chargers. Adopting the adaptive robust optimization (ARO), the authors in [7] put forward a model to optimally schedule an active distribution network (ADN) composed of RDGs and flexible sources (FSs). An optimization model with two objective functions is used in the deterministic programming to minimize the difference between the operating costs of the network and NRDGs and the revenue of the RDG, NRDG, and FS gained by selling active and reactive power (the first objective function).…”

Section: Iintroductionmentioning

confidence: 99%

See 1 more Smart Citation

A Framework of Electricity Market Based on Two-Layer Stochastic Power Management for Microgrids

et al. 2022

View full text Add to dashboard Cite

This article develops a novel multi-microgrids (MMGs) participation framework in the dayahead energy and ancillary services, i.e. services of reactive power and reserve regulation, markets incorporating the smart distribution network (SDN) objectives based on two-layer power management system (PMS). A bi-level optimization structure is introduced wherein the upper level models optimal scheduling of SDN in the presence of MMGs while considering the bilateral coordination between microgrids (MGs) and SDN's operators, i.e. second layer's PMS. This layer is responsible for minimizing energy loss, expected energy not-supplied, and voltage security as the sum of weighted functions. In addition, the proposed problem is subject to linearized AC optimal power flow (LAC-OPF), reliability and security constraints to make it more practical. Lower level addresses participation of MGs in the competitive market based on bilateral coordination among sources, active loads and MGs' operator (first layer's PMS). The problem formulation then tries to minimize the difference between MGs' cost and revenue in markets while satisfying constraints of LAC-OPF equations, reliability, security, and flexibility of the MGs. Karush-Kuhn-Tucker method is exploited to achieve a single-level model. Moreover, a stochastic programming model is introduced to handle the uncertainties of load, renewable power, energy price, the energy demand of mobile storage, and availability of network equipment. The simulation results confirm the capabilities of the suggested stochastic two-layer scheme in simultaneous evaluation of the optimal status of different technical and economic indices of the SDN and MGs.INDEX TERMS Two-layer power management system, Energy and ancillary services markets, Multi-criteria objectives, Multi-microgrids, Multi-objective bi-level optimization.

show abstract

Section: Iimodel Of Proposed Problemmentioning

confidence: 99%

Section: Iintroductionmentioning

confidence: 99%

A Framework of Electricity Market Based on Two-Layer Stochastic Power Management for Microgrids

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The terms nr and nl represent the number of RESs and loads, respectively. The set of uncertainties for the ith row of the matrix u is defined as follows [33]:…”

Section: A Modeling Of Uncertainties Based On the Ea-aro Techniquementioning

confidence: 99%

“…The robust model solution is optimal only in the worst-case scenario, and this scenario was obtained from the U set. The optimal robust solution and the worst-case scenario were determined simultaneously with the ARO and/or the EA-ARO technique [33]. To better understand this, assume that the deterministic problem model is: (28) In the above problem, z and y are the problem variables, where it was assumed that the value of the variable z is independent of the value of the uncertain parameter ( u ), known as here and now [7].…”

Section: A Modeling Of Uncertainties Based On the Ea-aro Techniquementioning

confidence: 99%

See 1 more Smart Citation

Evolutionary Algorithm-Based Adaptive Robust Optimization for AC Security Constrained Unit Commitment Considering Renewable Energy Sources and Shunt FACTS Devices

Baziar

Ghotbabadi

et al. 2021

IEEE Access

View full text Add to dashboard Cite

An AC security constrained unit commitment (AC-SCUC) in the presence of the renewable energy sources (RESs) and parallel flexible AC transmission system (FACTS) devices is conventionally modeled as a deterministic optimization problem to minimize the operation cost of conventional generation units (CGUs) subject to AC optimal power flow (AC-OPF) equations, operation constraints of RESs, parallel FACTS devices, and CGUs. To cope with the uncertainties of load and RES generation, robust and stochastic optimization and linearized formulation have been used to achieve a sub-optimal solution. To achieve the optimal solution, this paper proposed an evolutionary algorithm-based adaptive robust optimization (EA-ARO) approach to solve the non-linear and non-convex optimization problem. A hybrid solver of grey wolf optimization (GWO) and teaching learning-based optimization (TLBO) obtained the robust and reliable optimal solution for the proposed AC-SCUC in the worst-case scenario. Finally, the proposed method was simulated on standard IEEE test systems to demonstrate its capabilities, and the results showed the proposed hybrid solver obtained robust optimal solutions with reduced computation time and standard deviation. Moreover, the numerical results proved the proposed strategy's capabilities of improving the economics of generation units, such as lower operational cost, and technical performance of the transmission networks, such as improved voltage profile and reduced energy losses.INDEX TERMS AC security constrained unit commitment, Evolutionary algorithm-based adaptive robust optimization, Renewable energy sources, Parallel FACTS devices.

show abstract

Energy loss-constrained optimal operation of smart distribution system with reactive power services from electric vehicles

Kharra,

Tiwari,

Singh

et al. 2023

Electr Eng

View full text Add to dashboard Cite

Adaptive robust operation of the active distribution network including renewable and flexible sources

Cited by 42 publications

References 20 publications

A Framework of Electricity Market Based on Two-Layer Stochastic Power Management for Microgrids

A Framework of Electricity Market Based on Two-Layer Stochastic Power Management for Microgrids

Evolutionary Algorithm-Based Adaptive Robust Optimization for AC Security Constrained Unit Commitment Considering Renewable Energy Sources and Shunt FACTS Devices

Energy loss-constrained optimal operation of smart distribution system with reactive power services from electric vehicles

Contact Info

Product

Resources

About