CERES :: Browsing by Author "Huo, Da"

Browsing by Author "Huo, Da"

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Open Access
Ageing mitigation for battery energy storage system in electric vehicles
(IEEE, 2022-09-27) Li, Shuangqi; Zhao, Pengfei; Gu, Chenghong; Li, Jianwei; Huo, Da; Cheng, Shuang
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.
Open Access
An improved energy management system framework for solar energy integration.
(Cranfield University, 2024-05) Falope, Tolulope Olumuyiwa; Lao, Liyun; Huo, Da
Renewable energy sources like wind and solar play a crucial role in decarbonizing energy supply, but their variable and intermittent nature lead to reliability and stability issues. One way of sustainably integrating these energy sources into the grid is through an energy management system. The study reported in this thesis gives a comprehensive definition of an integrated energy management system and creates a novel framework that identifies energy forecasting, demand-side management, and supply-side management, as crucial components for grid balancing. In addition, this research looks particularly at solar integration, and how the integrated energy management system offers a unique combination of solar energy forecasting, time-of-use tariffs, direct load control demand response, and generator control, in increasing penetration levels of solar energy. The significance of this research is that the proposed system presents a viable, sustainable, and cheaper way of increasing PV usage and thereby grid penetration by prioritising efficient use of available PV supply before calling up additional supply. To validate the proposed integrated energy management system, this research looks to understand the functions of each individual component and how their interconnectedness creates a novel management system. Firstly, this research develops a three-step solar forecasting approach that uses low-level data fusion to combine weather variables from both an on-site and a local weather station to improve solar energy forecasting. The forecasting model response is historic PV generation, and the predictors are weather variables with moderate to strong positive correlations to solar radiation. Data obtained is preprocessed using Low-level Data Fusion, Pearson Correlation Coefficient analysis, Rescaling method, and List-wise Deletion method. This approach is then tested on a 1MW utility scale solar plant, resulting in a 6% and 13% prediction accuracy improvement when compared to solely using data from an on-site, and local weather stations respectively. This approach is also validated for three residential rooftop solar systems (8 kW, 10.5 kW and 15 kW), achieving root mean square error values of 0.0984, 0.1425, and 0.0885 respectively. The resulting low root mean square error values, a measure of the predicted PV to actual PV generation, proves that the model can be adopted for different PV plant sizes and is suitable for any customer across the distributed generation spectrum. To further improve the accuracy of the model, other preprocessing techniques are investigated and applied. The study shows that the combination of Low-level Data Fusion, Linear Interpolation, filling outliers, data smoothing, Rescaling method, moderate to strong PV correlation of weather parameters using Pearson Correlation Coefficient, day/time/month decomposition, seasonal decomposition, Principal Component Analysis, and holdout validation, increases the accuracy of the model by 75%. The ability of direct load control to manage energy consumption is validated in a case study by using Connected Power’s unique smart sockets and Lumen radio’s Mira Mesh Radio Frequency wireless network. Small plug-in loads were connected to ten smart sockets located in a robotics laboratory and a café, resulting in reduced energy consumption by 44% and 72% respectively when compared to the baseline without direct load control. Finally, the integrated energy management system framework is validated by testing its capacity to increase PV usage for an off-grid residential house with a PV/diesel generator power source. A decision-based algorithm is created that adjusts PV supply forecast errors, initiates direct load control responses to reduce excess load during periods of low PV supply, and/or increase power supply by calling up a diesel generator. In addition, this is combined with the proposed three-step solar energy forecasting approach and a programmable load schedule based on time-of-use criteria. The effects of customer behaviour are also analysed by using a 14% override rate, with 80% preconditioning and 20% rebounding. The hybrid PV/diesel generator power source with the proposed integrated energy management system is compared against two configurations: a baseline configuration that uses a solely diesel generator source, and a hybrid PV/diesel generator power source. Results show that the integrated energy management system reduced the lifetime expenditure costs and CO2 emissions by 44% and 46% respectively when compared to the baseline configuration, and by 8% and 9% in the hybrid photovoltaic/diesel generator, while also increasing the PV usage from this configuration by over 113%. This research also addresses opportunities and limitations of the proposed system and lays the foundation for future research using other intermittent renewable energy sources such as wind.
Open Access
Development of an integrated energy management system for off-grid solar applications with advanced solar forecasting, time-of-use tariffs, and direct load control
(Elsevier, 2024-06-19) Falope, Tolulope Olumuyiwa; Lao, Liyun; Huo, Da; Kuang, Boyu
Effectively managing and maximizing the integration of renewable energy sources is essential for a sustainable power grid due to the stochastic and intermittent nature of renewable energy generation. This study develops a comprehensive Integrated Energy Management System incorporating supply-demand side management in the form of time-of-use credit, direct load control, and generator control to enhance photovoltaic utilization in off-grid applications. A novel three-step solar energy forecasting approach is proposed in this paper, utilizing low-level data fusion and regression models to predict next-day photovoltaic generation with improved accuracy, and a rule-based decision algorithm is developed to correct forecast errors and manage loads dynamically. A techno-economic analysis covering a 20-year duration is carried out for scenarios with and without the integrated energy management system; three configurations are investigated for supplying an off-grid residential home, including diesel generator, diesel generator/photovoltaic system, and diesel generator/photovoltaic system/integrated energy management system. Results reveal that the hybrid configuration with integrated energy management system achieved 44 % and 46 % reductions in costs and carbon dioxide emissions compared to the diesel generator alone, and 8 % and 9 % compared to the diesel generator/photovoltaic setup respectively. The Integrated Energy Management System further enhanced photovoltaic utilisation rate by over 113 % when compared to the diesel generator/photovoltaic system. Further evaluations include customer behaviour impacts, demonstrating that a fully automated system with 100 % compliance significantly outperforms systems with manual customer control, highlighting the detrimental effect of overrides on the efficiency of direct load control. The flexibility of the Integrated Energy Management System framework allows potential adaptation for on-grid applications, showcasing its utility in diverse operational contexts.
Open Access
Hybrid energy system integration and management for solar energy: a review
(Elsevier, 2024-01-12) Falope, Tolulope; Lao, Liyun; Hanak, Dawid; Huo, Da
The conventional grid is increasingly integrating renewable energy sources like solar energy to lower carbon emissions and other greenhouse gases. While energy management systems support grid integration by balancing power supply with demand, they are usually either predictive or real-time and therefore unable to utilise the full array of supply and demand responses, limiting grid integration of renewable energy sources. This limitation is overcome by an integrated energy management system. This review examines various concepts related to the integrated energy management system such as the power system configurations it operates in, and the types of supply and demand side responses. These concepts and approaches are particularly relevant for power systems that rely heavily on solar energy and have constraints on energy supply and costs. Building on from there, a comprehensive overview of current research and progress regarding the development of integrated energy management system frameworks, that have both predictive and real-time energy management capabilities, is provided. The potential benefits of an energy management system that integrates solar power forecasting, demand-side management, and supply-side management are explored. Furthermore, design considerations are proposed for creating solar energy forecasting models. The findings from this review have the potential to inform ongoing studies on the design and implementation of integrated energy management system, and their effect on power systems.
Open Access
Linearizing battery degradation for health-aware vehicle energy management
(IEEE, 2022-10-28) Li, Shuangqi; Zhao, Pengfei; Gu, Chenghong; Huo, Da; Li, Jianwei; Cheng, Shuang
The utilization of battery energy storage systems (BESS) in vehicle-to-grid (V2G) and plug-in hybrid electric vehicles (PHEVs) benefits the realization of net-zero in the energy-transportation nexus. Since BESS represents a substantial part of vehicle total costs, the mitigation of battery degradation should be factored into energy management strategies. This paper proposes a two-stage BESS aging quantification and health-aware energy management method for reducing vehicle battery aging costs. In the first stage, a battery aging state calibration model is established by analyzing the impact of cycles with various Crates and depth of discharges based on a semi-empirical method. The model is further linearized by learning the mapping relationship between aging features and battery life loss with a linear-in-the-parameter supervised learning method. In the second stage, with the linear battery life loss quantification model, a neural hybrid optimization-based energy management method is developed for mitigating vehicle BESS aging. The battery aging cost function is formulated as a linear combination of system states, which simplifies model solving and reduces computation cost. The case studies in an aggregated EVs peak-shaving scenario and a PHEV with an engine-battery hybrid powertrain demonstrate the effectiveness of the developed method in reducing battery aging costs and improving vehicle total economy. This work provides a practical solution to hedge vehicle battery degradation costs and will further promote decarbonization in the energy-transportation nexus.
Open Access
Online battery-protective vehicle to grid behavior management
(Elsevier, 2022-01-03) Li, Shuangqi; Zhao, Pengfei; Gu, Chenghong; Huo, Da; Zeng, Xianwu; Pei, Xiaoze; Cheng, Shuang; Li, Jianwei
With the popularization of electric vehicles, vehicle-to-grid (V2G) has become an indispensable technology to improve grid economy and reliability. However, battery aging should be mitigated while providing V2G services so as to protect customer benefits and mobilize their positivity. Conventional battery anti-aging V2G scheduling methods mainly offline operates and can hardly be deployed online in hardware equipment. This paper proposes a novel online battery anti-aging V2G scheduling method based on a novel two-stage parameter calibration framework. In the first stage, the V2G scheduling is modeled as an optimization problem, where the objective is to reduce grid peak-valley difference and mitigate battery aging. The online deployment of the developed optimization-based V2G scheduling is realized by a rule-based V2G coordinator in the second stage, and a novel parameter calibration method is developed to adjust controller hyper-parameters. With the parameter calibration process, the global optimality and real-time performance of V2G strategies can be simultaneously realized. The effectiveness of the proposed methodologies is verified on a practical UK distribution network. Simulation results indicate that it can effectively mitigate battery aging in providing V2G services while guaranteeing algorithm real-time performance.
Open Access
Re-evaluating drought indicators: learning from small-scale farmers in South Africa
(Elsevier, 2024-10-15) Shrimpton, Elisabeth A.; Balta-Ozkan, Nazmiye; Sarmah, Tanaya; Huo, Da; Marais, Lochner
Open Access
A reliability-aware chance-constrained battery sizing method for island microgrid
(Elsevier, 2022-04-14) Huo, Da; Santos, Marcos; Sarantakos, Ilias; Resch, Markus; Wade, Neal; Greenwood, David
Island Microgrids can coordinate local energy resources, provide post-fault reliability improvements for local customers, and aggregate local power and energy resources to offer services to the wider system. A crucial component of an Island Microgrid is the battery energy storage system, which can manage local imbalances, alleviate constraints, and improve reliability by enabling post-fault islanding. A planning and sizing method is required to quantify and maximize the benefits of battery energy storage while avoiding over-investment and under-utilization. This paper combines comprehensive reliability assessment with chance-constrained convex optimization, via second-order cone programming, to optimally size energy storage within an Island Microgrid. Chance constraints are applied to the battery state-of-charge to avoid sizing the energy storage to accommodate extreme cases of uncertainty, avoiding uneconomic investment. The probability of reaching a state-of-charge constraint also indicates the likelihood that the battery energy storage system will be unable to facilitate island operation in the event of an outage, which affects the Island Microgrid reliability. The method is demonstrated on a real Austrian distribution network as part of the MERLON project. Results illustrate that an optimal trade-off can be identified between system reliability and operating cost when the probability of violating the chance constraints is 4.8%.
Open Access
Socially governed energy hub trading enabled by blockchain-based transactions
(IEEE, 2023-09-05) Zhao, Pengfei; Li, Shuangqi; Cao, Zhidong; Hu, Paul Jen-Hwa; Gu, Chenghong; Yan, Xiaohe; Huo, Da; Luo, Tianyi; Wang, Zikang
Decentralized trading schemes involving energy prosumers have prevailed in recent years. Such schemes provide a pathway for increased energy efficiency and can be enhanced by the use of blockchain technology to address security concerns in decentralized trading. To improve transaction security and privacy protection while ensuring desirable social governance, this article proposes a novel two-stage blockchain-based operation and trading mechanism to enhance energy hubs connected with integrated energy systems (IESs). This mechanism includes multienergy aggregators (MAGs) that use a consortium blockchain and its enabled proof-of-work (PoW) to transfer and audit transaction records, with social governance principles for guiding prosumers’ decision-making in the peer-to-peer (P2P) transaction management process. The uncertain nature of renewable generation and load demand are adequately modeled in the two-stage Wasserstein-based distributionally robust optimization (DRO). The practicality of the proposed mechanism is illustrated by several case studies that jointly show its ability to handle an increased renewable generation capacity, achieve a 16.7% saving in the audit cost, and facilitate 2.4% more P2P interactions. Overall, the proposed two-stage blockchain-based trading mechanism provides a practical trading scheme and can reduce redundant trading amounts by 6.5%, leading to a further reduction of the overall operation cost. Compared to the state-of-the-art benchmark methods, our mechanism exhibits significant operation cost reduction and ensures social governance and transaction security for IES and energy hubs.
Open Access
Two-stage co-optimization for utility-social systems with social-aware P2P trading
(IEEE, 2022-08-30) Zhao, Pengfei; Li, Shuangqi; Hu, Paul Jen-Hwa; Cao, Zhidong; Gu, Chenghong; Yan, Xiaohe; Huo, Da; Hernando-Gil, Ignacio
Effective utility system management is fundamental and critical for ensuring the normal activities, operations, and services in cities and urban areas. In that regard, the advanced information and communication technologies underpinning smart cities enable close linkages and coordination of different subutility systems, which is now attracting research attention. To increase operational efficiency, we propose a two-stage optimal co-management model for an integrated urban utility system comprised of water, power, gas, and heating systems, namely, integrated water-energy hubs (IWEHs). The proposed IWEH facilitates coordination between multienergy and water sectors via close energy conversion and can enhance the operational efficiency of an integrated urban utility system. In particular, we incorporate social-aware peer-to-peer (P2P) resource trading in the optimization model, in which operators of an IWEH can trade energy and water with other interconnected IWEHs. To cope with renewable generation and load uncertainties and mitigate their negative impacts, a two-stage distributionally robust optimization (DRO) is developed to capture the uncertainties, using a semidefinite programming reformulation. To demonstrate our model’s effectiveness and practical values, we design representative case studies that simulate four interconnected IWEH communities. The results show that DRO is more effective than robust optimization (RO) and stochastic optimization (SO) for avoiding excessive conservativeness and rendering practical utilities, without requiring enormous data samples. This work reveals a desirable methodological approach to optimize the water–energy–social nexus for increased economic and system-usage efficiency for the entire (integrated) urban utility system. Furthermore, the proposed model incorporates social participations by citizens to engage in urban utility management for increased operation efficiency of cities and urban areas.

Browsing by Author "Huo, Da"

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