Integrated network partitioning and DERs allocation for planning of Virtual Microgrids

Wu, Qing; Xue, Fei; Lu, Shaofeng; Jiang, Lin; Huang, Tao; Wang, Xiaoliang; Sang, Yiyan

doi:10.1016/j.epsr.2022.109024

Cited by 8 publications

(3 citation statements)

References 45 publications

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“…Currently, metaheuristic algorithms seen a substantial history to achieve the optimal solution in fine-tuning machine learning algorithms and solving continuous optimization problems [27,28]. They have gained great popularity in different application domains such as smart grid applications [29][30][31], Green Building Energy-Optimization Systems (GBEOSs) [32], Electric Vehicles (EVs) [33][34][35], the Internet of Things (IoT) [36][37][38][39], the military domain [40][41][42], structural health monitoring applications [43,44], Resilient Cyber-Physical Systems (RCPSs) [45,46], robotics [47,48], etc. With the growth of solar energy systems and their various design and control aspects seen as complex problems, where metaheuristics are the best candidates for addressing them, PV cell parameter identification is becoming a wide research discipline, which involves artificial intelligence and bio-inspired optimization algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

An Accurate Metaheuristic Mountain Gazelle Optimizer for Parameter Estimation of Single- and Double-Diode Photovoltaic Cell Models

Abbassi,

Saidi,

Urooj

et al. 2023

Mathematics

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Accurate parameter estimation is crucial and challenging for the design and modeling of PV cells/modules. However, the high degree of non-linearity of the typical I–V characteristic further complicates this task. Consequently, significant research interest has been generated in recent years. Currently, this trend has been marked by a noteworthy acceleration, mainly due to the rise of swarm intelligence and the rapid progress of computer technology. This paper proposes a developed Mountain Gazelle Optimizer (MGO) to generate the best values of the unknown parameters of PV generation units. The MGO mimics the social life and hierarchy of mountain gazelles in the wild. The MGO was compared with well-recognized recent algorithms, which were the Grey Wolf Optimizer (GWO), the Squirrel Search Algorithm (SSA), the Differential Evolution (DE) algorithm, the Bat–Artificial Bee Colony Optimizer (BABCO), the Bat Algorithm (BA), Multiswarm Spiral Leader Particle Swarm Optimization (M-SLPSO), the Guaranteed Convergence Particle Swarm Optimization algorithm (GCPSO), Triple-Phase Teaching–Learning-Based Optimization (TPTLBO), the Criss-Cross-based Nelder–Mead simplex Gradient-Based Optimizer (CCNMGBO), the quasi-Opposition-Based Learning Whale Optimization Algorithm (OBLWOA), and the Fractional Chaotic Ensemble Particle Swarm Optimizer (FC-EPSO). The experimental findings and statistical studies proved that the MGO outperformed the competing techniques in identifying the parameters of the Single-Diode Model (SDM) and the Double-Diode Model (DDM) PV models of Photowatt-PWP201 (polycrystalline) and STM6-40/36 (monocrystalline). The RMSEs of the MGO on the SDM and the DDM of Photowatt-PWP201 and STM6-40/36 were 2.042717 ×10−3, 1.387641 ×10−3, 1.719946 ×10−3, and 1.686104 ×10−3, respectively. Overall, the identified results highlighted that the MGO-based approach featured a fast processing time and steady convergence while retaining a high level of accuracy in the achieved solution.

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Section: Literature Reviewmentioning

confidence: 99%

An Accurate Metaheuristic Mountain Gazelle Optimizer for Parameter Estimation of Single- and Double-Diode Photovoltaic Cell Models

Abbassi,

Saidi,

Urooj

et al. 2023

Mathematics

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“…For instance, with the introduction of peer-to-peer trading schemes, a consumer-producer (prosumer) is integrated into energy communities and the overall welfare of the community is optimized by coordinating resources and energy transactions with conventional grids [1], or the aggregation of distributed energy resources (DER) into multienergy virtual power plants, whose operation is economically optimized to provide services such as upstream reactive power support or frequency control [2]. Similarly, the problem of planning virtual microgrids (optimal partitions of a larger conventional distribution network) with the allocation of DER can be addressed with an integrated approach (instead of separating the problem) with the very same constraints imposed by the original AC-OPF [3]. Although the AC-OPF properly formulates a balanced steady-state problem for conventional networks, it has been also modified for the design of microgrids with Photovoltaic (PV) units and storage systems to overcome power intermittence from the utility to supply critical loads [4].…”

Section: Introductionmentioning

confidence: 99%

Convex Stochastic Approaches for the Optimal Allocation of Distributed Energy Resources in AC Distribution Networks with Measurements Fitted to a Continuous Probability Distribution Function

Osorio

Rosero

2023

Energies

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This paper addresses the optimal stochastic allocation of distributed energy resources in distribution networks. Typically, uncertain problems are analyzed in multistage formulations, including case generation routines, resulting in computationally exhaustive programs. In this article, two probabilistic approaches are proposed–range probability optimization (RPO) and value probability optimization (VPO)–resulting in a single-stage, convex, stochastic optimal power flow problem. RPO maximizes probabilities within a range of uncertainty, whilst VPO optimizes the values of random variables and maximizes their probabilities. Random variables were modeled with hourly measurements fitted to the logistic distribution. These formulations were tested on two systems and compared against the deterministic case built from expected values. The results indicate that assuming deterministic conditions ends in highly underestimated losses. RPO showed that by including ±10% uncertainty, losses can be increased up to 40% with up to −72% photovoltaic capacity, depending on the system, whereas VPO resulted in up to 85% increases in power losses despite PV installations, with 20% greater probabilities on average. By implementing any of the proposed approaches, it was possible to obtain more probable upper envelopes in the objective, avoiding case generation stages and heuristic methods.

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“…The model of power flow constraints, BESS model, and operation costs in these references are linearized. For the nonlinear model of power flow equation, BESS model, and operation costs, Wang et al (2021), Wu et al (2023) provide mixed-integer non-linear programming (MINLP) models to solve the proposed problems. These solutions are more accurate than solutions obtained from MILP, but are much more time-consuming.…”

Section: Introductionmentioning

confidence: 99%

Community microgrid planning in Lombok Island: an Indonesian case study

et al. 2023

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Rural electrification, diesel generator replacement, and resilient electrification systems against natural disasters are among the main targets for Perusahaan Listrik Negara (PLN) in Indonesia to achieve a universally accessible, resilient, and environment-friendly electricity supply. Microgrids, therefore, become a popular and available way to achieve the aforementioned targets due to their flexibility and resiliency. This paper aims to provide a resilience-oriented planning strategy for community microgrids in Lombok Island, Indonesia. A mixed-integer linear program, implemented in the distributed energy resources customer adoption model (DER-CAM), is presented in this paper to find the optimal technology portfolio, placement, capacity, and optimal dispatch in a community microgrid. The multinode model is adopted for the planning, and hence, power flow constraints, N-1 contingency, and technology constraints are considered. The results show that the placement of photovoltaic (PV) arrays, battery energy storage systems (BESSs), and diesel generators (DGs) as backup sources in multi-node community microgrids lead to multiple benefits, including 100% rural electrification, over 25% cost savings, as well as over 22%, in particular CO2 emission reduction in multinode community microgrids.

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Integrated network partitioning and DERs allocation for planning of Virtual Microgrids

Cited by 8 publications

References 45 publications

An Accurate Metaheuristic Mountain Gazelle Optimizer for Parameter Estimation of Single- and Double-Diode Photovoltaic Cell Models

An Accurate Metaheuristic Mountain Gazelle Optimizer for Parameter Estimation of Single- and Double-Diode Photovoltaic Cell Models

Convex Stochastic Approaches for the Optimal Allocation of Distributed Energy Resources in AC Distribution Networks with Measurements Fitted to a Continuous Probability Distribution Function

Community microgrid planning in Lombok Island: an Indonesian case study

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