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Thermodynamics
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Summary: This paper proposes a load-agent-based bilevel distributed coordination decision-making strategy for load aggregators participating in demand response, considering the risk caused by response uncertainty. Uncertainty models for reducible and transferable loads are established based on evidence theory, and an agent uncertainty model is created through clustering of individual user characteristics. The proposed strategy reduces computation time and lowers risk cost expectations after clustering processing.
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Summary: This study constructed input-output efficiency-evaluation indicators for urban green-energy development in 30 Chinese provinces using an improved data envelopment analysis model within the economic-energy-environmental framework. The results showed higher input-output efficiencies in provinces such as Jiangsu, Zhejiang, Fujian, Inner Mongolia, and Ningxia.
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Thermodynamics
Songrui Li et al.
Summary: With the rise of integrated energy systems, the role of load aggregator in connecting the demand and supply sides has become increasingly important. A bilateral integrated demand response model is established considering consumer psychology, using a hierarchical Stackelberg game. The proposed trading strategy increases the benefits of the integrated energy production base, load aggregator, and users, while reducing penalty costs. The hierarchical game model ensures sustainability and provides innovative ideas for the development of integrated energy systems.
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Economics
Atul Agrawal
Summary: This paper discusses the development of India's short term electricity market and the introduction of the Real Time Market (RTM). By addressing issues such as real-time power procurement and deviation settlement mechanism, the RTM has attracted significant attention from market participants and achieved notable success.
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Energy & Fuels
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Summary: This paper proposes a charging management scheme for charging and battery swapping stations, considering EV mobility patterns and grid constraints through interaggregator collaboration. The proposed framework improves wait time and scheduling, and takes into account the impact of PV support.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
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Qingyi Wang et al.
Summary: This paper tackles a general emergency supply planning problem and formulates it as a two-stage stochastic programming model to minimize total cost. The results show that considering traffic congestion effects and dynamic transportation plans can lead to better emergency supply plans.
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Maxensius Tri Sambodo et al.
Summary: The Indonesian government is incorporating Low-Carbon Development (LCD) into its National Medium-Term Development Plan 2020-2024. The study identifies technological and governance barriers as the most significant obstacles to LCD implementation in Indonesia. The research emphasizes the importance of collaboration between central and local governments in overcoming these barriers.
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Thermodynamics
Gayan Lankeshwara et al.
Summary: This paper proposes a hierarchical control scheme for inverter-type air conditioners to provide real-time market services while preserving end-user privacy and comfort. The scheme achieves privacy and scalability through the adoption of the alternating direction method of multipliers (ADMM) framework. Experimental results show that the proposed approach can achieve desired reference tracking and regulate indoor temperature, demonstrating robustness and scalability.
Article
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Daeho Kim et al.
Summary: As distributed energy resources (DERs) continue to emerge, the virtual power plant (VPP), a new cloud-based information technology platform business model, is being introduced into the electricity market. The competitiveness of VPPs relies on data analytics and operational technologies, with the optimal bidding decision problem being a significant factor. This study formulates a Markov decision process model and applies stochastic dynamic programming to address the bidding decision problem in a VPP company in South Korea.
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Energy & Fuels
Arsalan Najafi et al.
Summary: This paper proposes a hybrid decentralized robust optimization-stochastic programming (DRO-SP) model to coordinate the management of EVAGG and EH. By combining stochastic programming and robust optimization, uncertainties in entity scheduling are addressed, achieving economic scheduling while ensuring information privacy between entities.
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Energy & Fuels
Saad Ullah Khan et al.
Summary: This paper proposes a coordination method for multiple electric vehicle (EV) aggregators to smooth the system load profile by adjusting peak demands and charging during off-peak periods. The results show that the system load profile is smoothed under peak shaving and valley filling goals while ensuring EVs are fully charged before departure.
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Engineering, Electrical & Electronic
H. J. Kim et al.
Summary: This study proposes a two-stage stochastic p-robust optimal energy trading management for microgrid, utilizing a hybrid demand response and multi-scenario tree method to improve efficiency and reliability.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2021)
Article
Energy & Fuels
Giacomo Talluri et al.
Summary: This work proposes a strategy for scheduling a battery energy storage system in a renewable energy community, utilizing machine learning and linear programming algorithms to maximize economic benefits and minimize operating costs of the community. The validation of the strategy on real data shows a 10% increase in average revenue for individual prosumers compared to non-optimized scenarios by reducing BESS usage by around 30%.
Article
Thermodynamics
Xi Lu et al.
Summary: This paper proposes a comprehensive two-stage robust distribution system operation model that can effectively handle uncertainties in electric vehicle aggregators (EVAs), and solves it using a constraint generation algorithm with mathematical proof, indicating good performance of the model.
Article
Energy & Fuels
Xinyi Chen et al.
Summary: In demand response programs, a optimization problem of scheduling customers considering fatigue based on the MAB framework has been proposed, and a method called fatigue-aware MAB is presented to solve it. Numerical simulations show that the proposed method performs better than the traditional method under different parameter settings, with detailed analysis provided on how these settings affect the performance.
Article
Energy & Fuels
Sajad Karimi et al.
Summary: This research proposes a mathematical optimization-based approach to comprehensively evaluate and analyze the impact of energy-aware production scheduling, on-site solar power generation, and battery energy storage on energy cost and makespan in manufacturing systems. Numerical experiments show that utilizing all three technologies can significantly reduce energy cost and total cost, with a 36% savings in energy cost and a 15% savings in total cost when considering energy and time-related makespan costs. Sensitivity analysis is also conducted to assess the practical benefits of renewable energy generation and battery energy storage under different capacities.
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2020 IEEE STUDENT CONFERENCE ON ELECTRIC MACHINES AND SYSTEMS (SCEMS 2020)
(2020)
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Ran Wang et al.
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IEEE TRANSACTIONS ON SMART GRID
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