Adaptive Robust Optimization Research Articles (Page 2)

A comprehensive stochastic-based adaptive robust model for transmission expansion planning

A machine learning approach to two-stage adaptive robust optimization

A robust approach to food aid supply chains

Risk-constrained bidding and offering strategy for sector-coupled electricity-hydrogen systems incorporating accessibility level of mobility sector

Smart landscaping design for sustainable net-zero energy smart cities: Modeling energy hub in digital twin

Incorporating local uncertainty management into distribution system planning: An adaptive robust optimization approach

The increasing penetration of distributed generators (DGs) and the advancement of information and communication technologies (ICTs) will facilitate the transformation of the traditional passive distribution network towards a cyber-physical active distribution system (CPADS). With the increasing risks of extreme events, such as natural disasters (e.g., flooding) and cyber-physical attacks, it is critical for CPADS to formulate a restoration scheme to improve its resilience. Therefore, in this paper, a distributed adaptive robust restoration scheme with voltage/var control is presented to cope with the high-impact but low-frequency events. First, a detailed cyber-physical system model is established, including the dynamic routing and the quality-of-services (QoS) in both optical fiber networks and 5G wireless networks. Then, the interactions between the cyber system and the physical system are analyzed. Based on the cyber-physical system model, a two-stage restoration scheme with voltage/var control is proposed by coordinately scheduling different network assets in day-ahead and in real-time. The formulated problem is solved by adaptive robust optimization (ARO). To further enhance the resilience of the CPADS, a distributed restoration framework is proposed. The distributed problem is solved by the alternating direction method of multipliers (ADMM) algorithm, and the convergence of the discrete problem is ensured by introducing the alternating optimization procedure (AOP). Considering the cyber faults, a boundary variable compensation and residual relaxation mechanism is proposed in ADMM. The proposed framework and methodology are verified in the case study. The convergence and the efficiency of the proposed algorithm are verified. Compared with the state-of-art works, the advantages in load restoration capability of the proposed method are shown.

The rapid integration of electric vehicles (EVs) into power grids introduces substantial operational challenges, particularly in managing the grid and ensuring efficient energy distribution. Addressing these issues, this paper presents a novel tri-level adaptive robust optimization model for EV charging coordination, explicitly considering active and reactive power control (RPC) and its impact on the distribution network. The first level optimizes the operation of unbalanced three-phase AC distribution systems through economic dispatch of distributed generation (DG) units, modeled using mathematical programming with equilibrium constraints (MPEC). The second level minimizes the energy non-supplied (ENS) to EVs during charging, considering both grid constraints and DG dispatches. The third level introduces an adaptive robust approach to handle uncertainties related to demand, renewable energy generation, and EV initial states of charge. This tri-level model, formulated as a min-max-min optimization, is solved using the column-and-constraint generation (C&CG) method. Validation on 25-node and 123node systems equipped with dispatchable DG units, photovoltaic systems, and an EV fleet managed by a charging point operator (CPO) demonstrates the model's ability to mitigate uncertainties and balance conflicting interests between the CPO/aggregator and the distribution system operator (DSO). The results show that incorporating RPC reduces grid impact and ENS by up to 17.26% in the 25-node system and 30.68% in the 123-node system, highlighting the effectiveness of the proposed approach in enhancing grid resilience amidst increasing EV penetration. This work offers a comprehensive and scalable solution for private charging infrastructures, providing critical insights into improving grid resilience, optimizing EV charging operations, and effectively balancing the interests of both the CPO/aggregator and the DSO. INDEX TERMS Electric vehicle fleets, mathematical programming with equilibrium constraints, optimal charging coordination, tri-level adaptive robust optimization. The associate editor coordinating the review of this manuscript and approving it for publication was Ayaz Ahmad . F Set of phases, F = {A, B, C}. G Set of dispatchable distributed generation (DG) units. L Set of circuits.

Stochastic adaptive robust optimization is capable of handling short-term uncertainties in demand and variable renewable-energy sources that affect investment in generation and transmission capacity. We build on this setting by considering a multi-year investment horizon for finding the optimal plan for generation and transmission capacity expansion while reducing greenhouse gas emissions. In addition, we incorporate multiple hours in power-system operations to capture hydropower operations and flexibility requirements for utilizing variable renewable-energy sources such as wind and solar power. To improve the computational performance of existing exact methods for this problem, we employ Benders decomposition and solve a mixed-integer quadratic programming problem to avoid computationally expensive big-M linearizations. The results for a realistic case study for the Nordic and Baltic region indicate which investments in transmission, wind power, and flexible generation capacity are required for reducing greenhouse gas emissions. Through out-of-sample experiments, we show that the stochastic adaptive robust model leads to lower expected costs than a stochastic programming model under increasingly stringent environmental considerations.

An adaptive robust service composition and optimal selection method for cloud manufacturing based on the enhanced multi-objective artificial hummingbird algorithm

Energy storage plays an important role in integrating renewable energy sources and power systems, thus how to deploy growing distributed energy storage systems (DESSs) while meeting technical requirements of distribution networks is a challenging problem. This paper proposes an area-to-bus planning path with network constraints for DESSs under uncertainty. First, a distribution location marginal price (DLMP) formulation with maximum fluctuation boundaries of uncertainties is designed to select vulnerable areas exceeding voltage limits and higher line losses that occur in distribution networks. Different from simple multi-scenario power flow calculation and sensitivity analysis, DLMP with time and regional characteristics could be more intuitive to reflect line losses and voltage limits of distribution networks through price signals. After that, a two-stage stochastic robust optimization based planning method is developed to determine locations and capacities of DESSs in vulnerable areas. To make the uncertainty problem more tractable, stochastic scenarios are used to portray upper and lower boundaries of uncertainties, which avoids too-conservative decisions for robust optimization. Finally, numerical tests are implemented to testify the reasonability and validity of the proposed area-to-bus planning path under uncertainty. Compared with the DESSs planning framework without DLMP, the costs of DESSs are observably reduced with DLMP. With same budgets of uncertainty, investment costs of DESSs for the stochastic robust optimization with 30 and 50 scenarios are 3.91% and 4.45% lower than classical adaptive robust optimization (ARO).

Reactive power management considering Transmission System Operator and Distribution System Operator coordination

Adaptive robust scheduling optimization of a smart commercial building considering joint energy and reserve markets

Two methods for multistage adaptive robust binary optimization are investigated in this work. These methods referred to as binary decision rule and finite adaptability inherently share similarities in dividing the uncertainty set into subsets. In the binary decision rule method, the uncertainty is lifted using indicator functions which result in a nonconvex lifted uncertainty set. The linear decision rule is further applied to a convexified version of the lifted uncertainty set. In the finite adaptability method, the uncertainty set is divided into partitions and a constant decision is applied for each partition. In both methods, breakpoints are utilized either to define the indicator functions in the lifting method or to partition the uncertainty set in the finite adaptability method. In this work, we propose variable breakpoint location optimization for both methods. Extensive computational study on an illustrating example and a larger size case study is conducted. The performance of binary decision rule and finite adaptability methods under fixed and variable breakpoint approaches is compared.

Considering the increasing penetration of renewable energy sources (RESs) into power grids, adopting efficient energy management strategies is vital to mitigate the uncertainty issues resulting from the intermittent nature of their output power. This paper aims to resolve the energy management problem by presenting an adaptive robust optimization (ARO) model in which uncertainties associated with solar and wind powers, consumer demand, and electricity prices are considered. The proposed model comprises three stages, one master and two subproblems, using both linearized and convexified power flow equations. Meanwhile, a new optimization-based bound tightening (OBBT) method is also presented to strengthen the relaxation. The proposed solution method solves the microgrid (MG) operation management problem with high accuracy due to considering the convexification method in a reasonable computational time and reducing operational costs compared to conventional models. The numerical results indicate the benefits of the proposed ARO and solution approach over traditional methods.

Adaptive robust optimization for lot-sizing under yield uncertainty

Abstract High penetration of uncertain wind power generation brings challenges to power system operational security and economy. Here, an adjustable box uncertainty set is first presented to characterize the spatiotemporal correlation of multiple wind power generations. Afterward, a two‐stage risk‐adjusted robust energy dispatching model is established as a min‐max‐min optimization problem for active distribution networks operating with high penetration wind power generation. Then a three‐layer Nest Column and Constraint Generation (NCCG) decomposition approach is designed to efficiently solve the proposed model. Finally, the proposed two‐stage adaptive robust optimization model and NCCG approach are validated by a distribution network with uncertain wind power generations, and the simulation results indicate the distribution network operation economy could be compromised with robustness by adjusting the conservative regulation parameter.

A combined heat and power virtual power plant (CHP-VPP) can effectively control the distributed resources in an electric–thermal coupling system and solve the problem of lack of flexibility caused by large-scale renewable energy grid connection. Similar to the optimal reconfiguration of distribution network topology by operating switches, the district heating system is also equipped with tie and sectionalizing valves to realize the optimal adjustment of district heating network (DHN) topology, which provides an economical and effective method for improving the power system’s flexibility. Based on this, this paper proposes a CHP-VPP economic scheduling model considering reconfigurable DHN. Firstly, the energy flow model is introduced to reduce the computational complexity. Secondly, adaptive robust optimization solved by the column-and-constraint generation algorithm is used to settle the randomness of wind power to ensure that the results are feasible in all worst scenarios. Finally, the feasibility of the proposed model is illustrated by case studies based on an actual CHP-VPP. The results show that compared with the reference case, considering the reconfigurability of DHN in the CHP-VPP optimization scheduling process can reduce the cost by about 2.78%.

Expansion planning of the transmission network with high penetration of renewable generation: A multi-year two-stage adaptive robust optimization approach

The tradable flexibility certificate market policy for flexible investment incentive