Article
Green & Sustainable Science & Technology
Michael Hamwi, Iban Lizarralde, Jeremy Legardeur
Summary: This study focuses on demand response business models (DRBMs) to promote energy flexibility in a cost-efficient manner. A business model analytical framework with nine elements is proposed to explore demand response potential in electricity markets, aiding in recognition and creation of business models in emerging markets.
JOURNAL OF CLEANER PRODUCTION
(2021)
Article
Economics
Bowei Guo, Melvyn Weeks
Summary: With the increasing use of renewable energy in electricity generation, there will be a higher variability in residual demand, posing challenges to the stability and flexibility of power systems. Demand response, achieved through dynamic tariffs, is a possible solution where consumers are incentivized to shift or reduce peak load. By modeling the retail market using a two-stage dynamic game, we find that dynamic tariffs increase the retailer's profit and can benefit consumers and retailers under market regulations. Additionally, the interaction between demand-side management stimuli and market regulation can further decrease consumer-level electricity demand, increase retail profit, and lower consumers' electricity bills.
Article
Energy & Fuels
Ahmad Rezaee Jordehi
Summary: This research aims to develop a risk-averse two-stage stochastic model for the participation of heat and power virtual power plants (VPPs) in various markets. The study investigates the effect of different markets on VPP profit and risk, and explores the participation of VPPs in contracts with withdrawal penalty. The results show that export through contracts with withdrawal penalty increases expected profit, and the risk weight factor has a limited impact on profit. The study also evaluates the effect of perfect information on VPP profit.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Energy & Fuels
Zixia Pei, Yunlong Ma, Mengyun Wu, Jianlan Yang
Summary: This study explores the role and operation mode of load aggregators in the electricity market, as well as the associated uncertainties and strategies for addressing them. It discusses load integration methods and scheduling control strategies of load aggregators, and proposes suggestions for future research directions.
FRONTIERS IN ENERGY RESEARCH
(2021)
Article
Automation & Control Systems
Koichi Kobayashi, Kunihiko Hiraishi
Summary: This article presents a control-theoretic approach for an Automated Demand Response (ADR) program, with effectiveness demonstrated through modeling air conditioner power consumption, and introduces a new method of model predictive control. The scalability of the proposed method with respect to the number of consumers is emphasized.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2021)
Article
Computer Science, Information Systems
Jeseok Ryu, Jinho Kim
Summary: With the decentralization of power systems, the importance of resources for balancing electricity supply and demand is increasing. The role of demand response is being emphasized to effectively deal with the volatility of renewable energy. This study proposes a model to participate in the electricity market by utilizing demand response resources to compensate for the uncertainty of renewable energy products, and develops a demand response modeling approach to enhance flexibility.
Article
Engineering, Electrical & Electronic
Xian Wang, Jiaying Yang, Kai Zhang, Shaohua Zhang, Lei Wu
Summary: This paper presents a market-based operation mechanism for demand response resources to participate in the wholesale electricity market, utilizing a specific DR exchange market and incentive compensation method to encourage participation. The effectiveness of the mechanism is examined through a game-theoretic model, demonstrating the incentive for demand response aggregators to compete in the wholesale electricity market.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2022)
Article
Construction & Building Technology
Qiming Fu, Lu Liu, Lifan Zhao, Yunzhe Wang, Yi Zheng, You Lu, Jianping Chen
Summary: As urbanization continues to accelerate, effective management of peak electricity demand is crucial to avoid power outages and system overloads. In order to address this challenge, a novel model-free predictive control method called D2PC-DDPG is proposed, which combines deep reinforcement learning and optimal control of energy storage systems. Experimental results demonstrate the superior performance of the proposed method in prediction accuracy and control performance compared to traditional machine learning and reinforcement learning methods. The method also shows generalizability in reducing peak load in multiple regions.
JOURNAL OF BUILDING ENGINEERING
(2023)
Article
Economics
Parinya Sonsaard, Nipon Ketjoy, Yodthong Mensin
Summary: Demand Response (DR) is an energy management strategy that reduces electricity usage during high-demand periods, leading to cost and CO2 emissions reductions. This study proposes a DR business option where Load Aggregators (LAs) collect and manage committed DR resources, with state utilities as the lead LAs. The analysis shows that this business option is preferable based on market structure, financial analysis, and SWOT assessment.
Article
Energy & Fuels
Shiyu Yang, Oliver Gao, Fengqi You
Summary: Incorporating phase-change material wallboards into building envelopes can reduce heating electricity costs and enhance demand flexibility. This study proposes a model predictive control framework for price-based, demand-responsive control of PCM-wallboard-enhanced space heating systems to minimize costs and maximize flexibility.
Article
Energy & Fuels
Seongmun Oh, Jaesung Jung, Ahmet Onen, Chul-Ho Lee
Summary: This study focuses on the participation strategy of aggregators in the demand response program. By using the reinforcement learning framework, the aggregators and customers interact and make optimal decisions regarding incentives and energy storage system operations to optimize the overall system.
FRONTIERS IN ENERGY RESEARCH
(2022)
Article
Energy & Fuels
Gayan Lankeshwara, Rahul Sharma, Ruifeng Yan, Tapan K. Saha
Summary: The paper proposes a novel two-stage control algorithm for robust management of aggregate residential loads, ensuring precise load set-point tracking while preserving end-user thermal comfort. By utilizing air conditioners and water heaters as controllable loads, the study demonstrates the effectiveness of the approach in load management and mitigation of unknown uncertainties. Comparisons with existing industry approaches show that the proposed control scheme is resilient to uncertainties, maintains thermal comfort, and is practical under current demand response standards.
Article
Energy & Fuels
Marija Miletic, Mirna Grzanic, Ivan Pavic, Hrvoje Pandzic, Tomislav Capuder
Summary: Empowering end-users to change their behavior and providing flexibility is an important aspect of the EU Clean Energy legislative package. This paper investigates the benefits of automation and different electricity pricing options for households, as well as the impact on suppliers. The results show that automated households can reduce electricity bills through time-of-use pricing, and suppliers' revenue is mostly affected by households' local production.
SUSTAINABLE ENERGY GRIDS & NETWORKS
(2022)
Article
Construction & Building Technology
Zihao Zhao, Cuiling Wang, Baolong Wang
Summary: This study proposes an adaptive model predictive control method for demand response in building hot water production. The results demonstrate significant cost and energy savings compared to traditional methods, achieving optimal control in various situations.
ENERGY AND BUILDINGS
(2023)
Article
Economics
David Benatia
Summary: This article provides an in-depth quantitative study of the impacts of the pandemic on the French electricity sector. During the first lockdown episode, France experienced significant reductions in electricity demand and wholesale prices, resulting in substantial revenue losses for market participants. The author argues that these market outcomes during the crisis may indicate the potential outcomes in a future with abundant renewable power.
Article
Energy & Fuels
Shitong Fang, Houfan Du, Tao Yan, Keyu Chen, Zhiyuan Li, Xiaoqing Ma, Zhihui Lai, Shengxi Zhou
Summary: This paper proposes a new type of nonlinear VIV energy harvester (ANVEH) that compensates for the decrease in peak energy output at low wind speeds by introducing an auxiliary structure. Theoretical and experimental results show that ANVEH performs better than traditional nonlinear VIV energy harvesters under various system parameter variations.
Article
Energy & Fuels
Wei Jiang, Shuo Zhang, Teng Wang, Yufei Zhang, Aimin Sha, Jingjing Xiao, Dongdong Yuan
Summary: A standardized method was developed to evaluate the availability of solar energy resources in road areas, which combined the Analytic Hierarchy Process (AHP) and the Geographic Information System (GIS). By analyzing critical factors and using a multi-indicator evaluation method, the method accurately evaluated the utilization of solar energy resources and guided the optimal location selection for road photovoltaic (PV) projects. The results provided guidance for the application of road PV projects and site selection for route corridors worldwide, promoting the integration of transportation and energy.
Article
Energy & Fuels
Chang Liu, Jacob A. Wrubel, Elliot Padgett, Guido Bender
Summary: The study investigates the effects of coating defects on the performance of the anode porous transport layer (PTL) in water electrolyzers. The results show that an increasing fraction of uncoated regions on the PTL leads to decreased cell performance, with continuous uncoated regions having a more severe impact compared to multiple thin uncoated strips.
Article
Energy & Fuels
Marcos Tostado-Veliz, Xiaolong Jin, Rohit Bhakar, Francisco Jurado
Summary: In this paper, a coordinated charging price mechanism for clusters of parking lots is proposed. The research shows that enabling vehicle-to-grid characteristics can bring significant economic benefits for users and the cluster coordinator, and vehicle-to-grid impacts noticeably on the risk-averse character of the uncertainty-aware strategies. The developed pricing mechanism can reduce the cost for users, avoiding to directly translate the energy cost to charging points.
Article
Energy & Fuels
Duan Kang
Summary: Building an energy superpower is a key strategy for China and a long-term goal for other countries. This study proposes an evaluation system and index for measuring energy superpower, and finds that China has significantly improved its ranking over the past 21 years, surpassing other countries.
Article
Energy & Fuels
Fucheng Deng, Yifei Wang, Xiaosen Li, Gang Li, Yi Wang, Bin Huang
Summary: This study investigated the synergistic blockage mechanism of sand and hydrate in gravel filling layer and the evolution of permeability in the layer. Experimental models and modified permeability models were established to analyze the effects of sand particles and hydrate formation on permeability. The study provided valuable insights for the safe and efficient exploitation of hydrate reservoirs.
Article
Energy & Fuels
Hao Wang, Xiwen Chen, Natan Vital, Edward Duffy, Abolfazl Razi
Summary: This study proposes a HVAC energy optimization model based on deep reinforcement learning algorithm. It achieves 37% energy savings and ensures thermal comfort for open office buildings. The model has a low complexity, uses a few controllable factors, and has a short training time with good generalizability.
Article
Energy & Fuels
Moyue Cong, Yongzhuo Gao, Weidong Wang, Long He, Xiwang Mao, Yi Long, Wei Dong
Summary: This study introduces a multi-strategy ultra-wideband energy harvesting device that achieves high power output without the need for external power input. By utilizing asymmetry, stagger array, magnetic coupling, and nonlinearity strategies, the device maintains a stable output voltage and high power density output at non-resonant frequencies. Temperature and humidity monitoring are performed using Bluetooth sensors to adaptively assess the device.
Article
Energy & Fuels
Tianshu Dong, Xiudong Duan, Yuanyuan Huang, Danji Huang, Yingdong Luo, Ziyu Liu, Xiaomeng Ai, Jiakun Fang, Chaolong Song
Summary: Electrochemical water splitting is crucial for hydrogen production, and improving the hydrogen separation rate from the electrode is essential for enhancing water electrolyzer performance. However, issues such as air bubble adhesion to the electrode plate hinder the process. Therefore, a methodology to investigate the two-phase flow within the electrolyzer is in high demand. This study proposes using a microfluidic system as a simulator for the electrolyzer and optimizing the two-phase flow by manipulating the micro-structure of the flow.
Article
Energy & Fuels
Shuo Han, Yifan Yuan, Mengjiao He, Ziwen Zhao, Beibei Xu, Diyi Chen, Jakub Jurasz
Summary: Giving full play to the flexibility of hydropower and integrating more variable renewable energy is of great significance for accelerating the transformation of China's power energy system. This study proposes a novel day-ahead scheduling model that considers the flexibility limited by irregular vibration zones (VZs) and the probability of flexibility shortage in a hydropower-variable renewable energy hybrid generation system. The model is applied to a real hydropower station and effectively improves the flexibility supply capacity of hydropower, especially during heavy load demand in flood season.
Article
Energy & Fuels
Zhen Wang, Kangqi Fan, Shizhong Zhao, Shuxin Wu, Xuan Zhang, Kangjia Zhai, Zhiqi Li, Hua He
Summary: This study developed a high-performance rotary energy harvester (AI-REH) inspired by archery, which efficiently accumulates and releases ultralow-frequency vibration energy. By utilizing a magnetic coupling strategy and an accumulator spring, the AI-REH achieves significantly accelerated rotor speeds and enhanced electric outputs.
Article
Energy & Fuels
Yi Yang, Qianyi Xing, Kang Wang, Caihong Li, Jianzhou Wang, Xiaojia Huang
Summary: In this study, a novel hybrid Quantile Regression (QR) model is proposed for Probabilistic Load Forecasting (PLF). The model integrates causal dilated convolution, residual connection, and Bidirectional Long Short-Term Memory (BiLSTM) for multi-scale feature extraction. In addition, a Combined Probabilistic Load Forecasting System (CPLFS) is proposed to overcome the inherent flaws of relying on a single model. Simulation results show that the hybrid QR outperforms traditional models and CPLFS exceeds the best benchmarks in terms of prediction accuracy and stability.
Article
Energy & Fuels
Wen-Jiang Zou, Young-Bae Kim, Seunghun Jung
Summary: This paper proposes a dynamic prediction model for capacity fade in vanadium redox flow batteries (VRFBs). The model accurately predicts changes in electrolyte volume and capacity fade, enhancing the competitiveness of VRFBs in energy storage applications.
Article
Energy & Fuels
Yuechao Ma, Shengtie Wang, Guangchen Liu, Guizhen Tian, Jianwei Zhang, Ruiming Liu
Summary: This paper focuses on the balance of state of charge (SOC) among multiple battery energy storage units (MBESUs) and bus voltage balance in an islanded bipolar DC microgrid. A SOC automatic balancing strategy is proposed considering the energy flow relationship and utilizing the adaptive virtual resistance algorithm. The simulation results demonstrate the effectiveness of the proposed strategy in achieving SOC balancing and decreasing bus voltage unbalance.
Article
Energy & Fuels
Raad Z. Homod, Basil Sh. Munahi, Hayder Ibrahim Mohammed, Musatafa Abbas Abbood Albadr, Aissa Abderrahmane, Jasim M. Mahdi, Mohamed Bechir Ben Hamida, Bilal Naji Alhasnawi, A. S. Albahri, Hussein Togun, Umar F. Alqsair, Zaher Mundher Yaseen
Summary: In this study, the control problem of the multiple-boiler system (MBS) is formulated as a dynamic Markov decision process and a deep clustering reinforcement learning approach is applied to obtain the optimal control policy. The proposed strategy, based on bang-bang action, shows superior response and achieves more than 32% energy saving compared to conventional fixed parameter controllers under dynamic indoor/outdoor actual conditions.