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Article
Energy & Fuels
Chunning Song et al.
Summary: This paper proposes a two-stage distribution robust optimization (DRO) model for determining the optimal bidding strategy for virtual power plant (VPP) participation in the energy market and combines L1 norm with L8 norm to simultaneously constrain the confidence set of uncertain probability distributions. The robustness and feasibility of the proposed model are verified by a case study.
Article
Energy & Fuels
Guoyu Zhang et al.
Summary: This paper proposes the ORTCS strategy for collaborative optimization of electric vehicles and renewable energy output, taking into account the flexibility of charge and discharge in the intelligent transportation field. The strategy aims to maximize the consumption of renewable energy while minimizing waiting time and expenses for car users. It evaluates the organized planning of charge and discharge based on the initial battery SOC of an electric vehicle connected to the power system, in order to obtain the real-time schedulability level from the perspective of the entire charging station.
Article
Engineering, Electrical & Electronic
Ranjita Chowdhury et al.
Summary: This paper proposes a technique for determining the optimal placement of an Electric Vehicle Charging Station (EVCS) in a distribution system, taking into account time-driven load models. The technique also considers factors such as traffic and weather conditions, as well as the availability of EVCS. Using Dynamic Fault Tree Analysis and Bayesian optimization techniques, the initial State of Charge of the PEV is reformulated. The results are validated by comparing them with computation of three REDG assessment indices for arbitrary positioning of the elements.
ELECTRIC POWER SYSTEMS RESEARCH
(2023)
Article
Energy & Fuels
Changsheng Lin et al.
Summary: Private electric vehicles have the potential to provide emergency power supply to buildings. By upgrading charging piles to support bi-directional power supply, EVs can assist in the restoration of disaster-affected buildings. A planning method that considers uncertainties and uses a stochastic programming model is proposed to determine the upgrade plan.
Article
Energy & Fuels
Wen Lu et al.
Summary: This paper improves the traditional two-stage robust optimization model by generating extreme scenarios and incorporating all-scenario-feasibility and nonanticipativity constraints into the model, enabling it to better handle the uncertainty problem brought by renewable energy.
Article
Energy & Fuels
Jiajie Chen et al.
Summary: This paper establishes an optimal operation model for Electric Vehicles in Electric Power Aggregators based on Stackelberg Game Model to achieve a win-win situation between the grid and Electric Vehicles, reducing costs and load fluctuations.
Article
Thermodynamics
Meysam Khojasteh et al.
Summary: The study introduces a new model to assess the performance of BES in various markets and determines optimal participation levels based on market prices, ramp rates, marginal costs, and technical constraints of units.
Article
Energy & Fuels
Yuechuan Tao et al.
Summary: This paper proposes a data-driven two-stage charging/swapping service scheme for electric vehicles (EVs), offering multiple service options for EV owners. The large-scale sequential decision-making problem is addressed using deep reinforcement learning and mixed-integer linear programming. Simulation results show that the proposed method performs better in relieving the burden of the power sector and improving daily revenue, answer rate, and queue length at fast-charging stations.
Review
Thermodynamics
Haifeng Qiu et al.
Summary: This paper reviews the research on two stage robust optimization (TSRO) scheduling of power systems, which is an effective approach in dealing with the uncertainties in optimal power system scheduling. It summarizes the formulations and solution algorithms of multi-type TSRO models, generalizes various modeling methods for continuous and discrete uncertainties, and reviews the achievements of TSRO in unit commitment, economic dispatch, active/reactive power coordination and resilient dispatch. The paper also discusses the existing research gaps and provides an outlook for future work.
Article
Thermodynamics
Younes Noorollahi et al.
Summary: Due to the importance of economic and environmental issues in the energy field, the development of smart energy systems and energy hubs has been accelerated. This research proposes a scenario-based approach for the optimal operation of energy hubs considering uncertainties in wind turbines and photovoltaics. The research findings suggest that Structure 3 (AC thorn HP) is more efficient in terms of energy consumption and investment costs when considering investment and operation and maintenance costs.
Article
Energy & Fuels
Jiang Tian et al.
Summary: This article proposes a method for predicting the orderly charging load of electric vehicles based on the characteristics of EV charging behavior and nonlinear programming. It utilizes the Monte Carlo algorithm to predict the charging probability and disorderly charging load of EVs, and employs a nonlinear programming algorithm to solve the optimized target function of orderly charging, thereby achieving the prediction of orderly charging load in the region.
Article
Energy & Fuels
Kang Wang et al.
Summary: This paper proposes an EV cluster scheduling strategy considering real-time electricity prices based on deep reinforcement learning. It effectively addresses the curse of dimensionality problem and the challenges of uncertainty in user demand and electricity price, achieving a win-win situation between the power grid and EV users.
Article
Energy & Fuels
Ran Ding et al.
Summary: This paper summarizes the development and present situation of battery swapping station and analyzes the distribution probability model of EVs' arrival. By combining the particle swarm optimization algorithm with the immune algorithm, the convergence accuracy of the traditional algorithm is improved. An economic scheduling method based on monte carlo simulation is proposed, and the function of BSS as an energy storage device is analyzed. A mathematical model of multi-objective joint optimization is established and the simulation analysis verifies the effectiveness of this model.
Article
Green & Sustainable Science & Technology
F. Fallahi et al.
Summary: This paper proposes a condition-based maintenance and operations scheduling approach that utilizes real-time sensor data and considers dependencies and uncertainties in wind farm operations. Experimental results demonstrate significant improvements in asset availability, market revenue, and maintenance costs.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2022)
Article
Energy & Fuels
Quan Sui et al.
Summary: When offshore-island renewable distribution systems (OIRDSs) face multiple failures during extreme weather conditions, the battery charging-swapping system (BCSS) is a promising solution to reduce power outage losses and enhance the resilience of OIRDSs. However, extreme weather, such as heavy rain, can affect logistics speed and result in non-integer-hour battery scheduling time between stations. To address this issue, a novel OIRDS resilience enhancement strategy is proposed that analyzes the impact of rainfall, solar radiation, and wind speed on renewable generation and battery transfer time. The strategy also presents a dual unit commitment method to coordinate scheduling steps and non-integer-hour battery transfer time, as well as an optimal OIRDS resilience enhancement model that integrates battery scheduling and network reconfiguration decisions.
Article
Energy & Fuels
Ying-Yi Hong et al.
Summary: This study combines chance-constrained programming with goal programming to optimize risk-based unit commitment (UC). It explores the use of battery energy storage systems (BESS) in flexible operations to reduce risk costs. The research finds that BESS significantly reduces risk costs in a realistic Taiwan power system in 2025.
Article
Energy & Fuels
F. A. Biggins et al.
Summary: Energy storage aggregation offers various advantages to electrical power systems, such as enhanced flexibility, cost reduction, and decreased reliance on carbon-intensive peaking plants. This study examines the value obtained by an aggregator using energy storage for arbitrage in Great Britain's day-ahead and balancing energy markets, employing linear and quadratic optimization models. The findings demonstrate that optimal arbitrage strategies depend on whether the aggregator acts as a price-taker or price-maker, and the availability of up to 500 MW battery storage. Additionally, the analysis highlights the significant impact of battery efficiency and duration on profits, and suggests that community-owned energy storage can positively impact household electricity bills.
Article
Thermodynamics
Mingze Zhang et al.
Summary: This paper proposes a double-stage coordinative decision-making framework for battery swapping and charging stations (BSCSs), using distributed robust optimization for multi-timescale battery inventories to maximize annual income. The framework enhances scheduling flexibility of BSCSs, improves regional load characteristics, and has been tested and verified through extensive simulation and comparison studies.
Article
Engineering, Electrical & Electronic
Hui Ren et al.
Summary: The electric vehicle, when managed by an aggregator, is a suitable candidate for participating in demand response in power system operation. An optimal scheduling algorithm is necessary to maximize the aggregator's benefit, considering the benefits of distribution system, aggregator, and EV users. This study defines the benefits of the three parties and tests the optimal scheduling system to analyze the impact of aggregator facilities, technology, and operation mode on the benefits.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2021)
Article
Green & Sustainable Science & Technology
Hou Sheng Zhou et al.
Summary: This paper presents an algorithm for optimizing the sizing and operation of battery energy storage systems in commercial buildings, aiming to coordinate and optimize the systems based on the value of reducing demand peaks.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2021)
Article
Engineering, Electrical & Electronic
Kenneth Bruninx et al.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2020)
Article
Thermodynamics
Xiaoxing Lu et al.
Article
Engineering, Electrical & Electronic
Arman Oshnoei et al.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2020)
Article
Engineering, Electrical & Electronic
Yu Wang et al.
IEEE TRANSACTIONS ON SMART GRID
(2019)
Article
Thermodynamics
Mohsen Mahoor et al.
Article
Computer Science, Information Systems
Salem Said et al.
IEEE TRANSACTIONS ON INFORMATION THEORY
(2017)
Article
Engineering, Industrial
Cansheng Wei et al.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2011)
Article
Management
D Bertsimas et al.
OPERATIONS RESEARCH
(2006)
