Online Battery Protective Energy Management for Energy-Transportation Nexus

et al. 2023

IEEE Trans. Smart Grid

Battery energy storage systems (BESS) have been extensively investigated to improve the efficiency, economy, and stability of modern power systems and electric vehicles (EVs). However, it is still challenging to widely deploy BESS in commercial and industrial applications due to the concerns of battery aging. This paper proposes an integrated battery life loss modeling and anti-aging energy management (IBLEM) method for improving the total economy of BESS in EVs. The quantification of BESS aging cost is realized by a multifactorial battery life loss quantification model established by capturing aging characteristics from cell acceleration aging tests. Meanwhile, a charging event analysis method is proposed to deploy the built life loss model in vehicle BESS management. Two BESS active anti-aging vehicle energy management models: vehicle to grid (V2G) scheduling and plug-in hybrid electric vehicle (PHEV) power distribution, are further designed, where the battery life loss quantification model is used to generate the aging cost feedback signals. The performance of the developed method is validated on a V2G peak-shaving simulation system and a hybrid electric vehicle. The work in this paper presents a practical solution to quantify and mitigate battery aging costs by optimizing energy management strategies and thus can further promote transportation electrification.

“…Scheduling constraints are set to subject the output power of the electric motor (28) and internal combustion engine (29). Furthermore, the battery SoC state is constrained by (30).…”

Section: B Battery Anti-aging Phev Energy Managementmentioning

confidence: 99%

Aging Mitigation for Battery Energy Storage System in Electric Vehicles

et al. 2023

IEEE Trans. Smart Grid

“…Another dualization is required to resolve the 'max' problem in (69). Therefore, the dual form of equations (68)(69)(70)(71)(72)(73)(74) is given as: min , , , , Finally, we have eliminated the min-max structure based on the original two-stage formulation (34)- (35). Problem ( 75)-( 80) is a tractable formulation of ( 53)-( 55), which can be solved by an off-the-shelf optimization commercial solver.…”

Section: B Tractable Mathematical Reformulationsmentioning

confidence: 99%

“…The parameters trading amount is summarized in TABLE Ⅱ [30,71]. The PV generation and load profile are estimated with a long short-term memory recurrent neural network [72,73], using the hourly data from the SoLa BRISTOL project in Bath and Bristol, United Kingdom [74,75]. This project aims to design a smart energy usage pattern for both general consumers and distribution systems, which can increase energy efficiency, reduce energy bills, solve essential network harmonics issues, phase distortions, and improve voltage controls.…”

Section: A Simulation Setupmentioning

confidence: 99%

Blockchain-Based Water-Energy Transactive Management With Spatial-Temporal Uncertainties

et al. 2023

IEEE Trans. Smart Grid

Self Cite

Water resources are vital to the energy conversion process but few efforts have been devoted to the joint optimization problem which is fundamentally critical to the water-energy nexus for small-scale or remote energy systems (e.g., energy hubs). Traditional water and energy trading mechanisms depend on centralized authorities and cannot preserve security and privacy effectively. Also, their transaction process cannot be verified and is subject to easy tampering and frequent exposures to cyberattacks, forgery, and network failures. Toward that end, water-energy hubs (WEHs) offers a promising way to analyse water-energy nexus for greater resource utilization efficiency.We propose a two-stage blockchain-based transactive management method for multiple, interconnected WEHs. Our method considers peer-topeer (P2P) trading and demand response, and leverages blockchain to create a secure trading environment. It features auditing and resource transaction record management via system aggregators enabled by a consortium blockchain, and entails spatial-temporal distributionally robust optimization (DRO) for renewable generation and load uncertainties. A spatial-temporal ambiguity set is incorporated in DRO to characterize the spatial-temporal dependencies of the uncertainties in distributed renewable generation and load demand. We conduct a simulation-based evaluation that includes robust optimization and the moment-based DRO as benchmarks. The results reveal that our method is consistently more effective than both benchmarks. Key findings include i) our method reduces conservativeness with lower WEH trading and operation costs, and achieves important performance improvements by up to 6.1%; and ii) our method is efficient and requires 18.7% less computational time than the moment-based DRO. Overall, this study contributes to the extant literature by proposing a novel two-stage blockchain-based WEH transaction method, developing a realistic spatialtemporal ambiguity set to effectively hedge against the uncertainties for distributed renewable generation and load demand, and producing empirical evidence suggesting its greater effectiveness and values than several prevalent methods., /Max & min buying power between WEHs.

“…In online methods, the charging behaviors of each GEV can be dynamically scheduled because the established control models are free of complex optimization processes [8,9]. The rapidity makes it possible to respond to the volatility of renewable energy.…”

Section: Introductionmentioning

confidence: 99%

Battery Protective Electric Vehicle Charging Management in Renewable Energy System

et al. 2023

IEEE Trans. Ind. Inf.

The adoption of grid-connected electric vehicles (GEVs) brings a bright prospect for promoting renewable energy. An efficient vehicle-to-grid (V2G) scheduling scheme that can deal with renewable energy volatility and protect vehicle batteries from fast aging is indispensable to enable this benefit. This paper develops a novel V2G scheduling method for consuming local renewable energy in microgrids by using a mixed learning framework. It is the first attempt to integrate battery protective targets in GEVs charging management in renewable energy systems. Battery safeguard strategies are derived via an offline soft-run scheduling process, where V2G management is modeled as a constrained optimization problem based on estimated microgrid and GEVs states. Meanwhile, an online V2G regulator is built to facilitate the real-time scheduling of GEVs' charging. The extreme learning machine (ELM) algorithm is used to train the established online regulator by learning rules from soft-run strategies. The online charging coordination of GEVs is realized by the ELM regulator based on real-time sampled microgrid frequency. The effectiveness of the developed models is verified on a UK microgrid with actual energy generation and consumption data. This work can effectively enable V2G to promote local renewable energy with battery aging mitigated, thus economically benefiting EV owns and microgrid operators, and facilitating decarbonization at low costs.