Browsing by Author "Long, Chao"

Now showing 1 - 12 of 12
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    Allocation method of coupled PV-energy storage-charging station in hybrid AC/DC distribution networks balanced with economics and resilience
    (Wiley, 2023-11-22) Ma, Ziyao; Zhang, Lu; Cai, Yongxiang; Tang, Wei; Long, Chao
    The hybrid AC/DC distribution network has become a research hotspot because of the wide access to multiple sources and loads. Meanwhile, extreme disasters in the planning period cause huge losses to the hybrid AC/DC distribution networks. A coupled PV-energy storage-charging station (PV-ES-CS) is an efficient use form of local DC energy sources that can provide significant power restoration during recovery periods. However, over investment will happen if too many PV-ES-CSs are installed. Therefore, it is important to determine the optimal numbers and locations of PV-ES-CS in hybrid AC/DC distribution networks balanced with economics and resilience. Firstly, the advantages of PV-ES-CS in normal operation and extreme disasters are analysed and the payment function is quantified accurately. Secondly, a bi-level optimal allocation model of PV-ES-CS in hybrid AC/DC distribution networks is established. In this model, the payment function using Nash equilibrium to balance economics and resilience is addressed at the upper-level, and the typical scenarios are simulated, and the optimal results are obtained using the genetic algorithm in lower level. Finally, a series of examples are analysed, which demonstrate the necessity of balancing economics and resilience, and advantages of DC lines in network restoration after disasters.
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    An anti-fraud double auction model in vehicle-to-vehicle energy trading with the k-factor approach
    (IEEE, 2024-05-01) Xu, Yiming; Zhang, Lu; Ozkan, Nazmiye; Long, Chao
    The rise in electric vehicle adoption has reduced greenhouse gas emissions in transportation but overloads the power grid due to charging demands. This paper introduces a Double Auction (DA) model in Vehicle-to-Vehicle (V2V) energy trading with the K-factor approach. The novel approach defines unique market clearing prices for each successfully matched V2V transaction pairs, robustly counteracts potential economic fraud. It overcomes shortcoming of some other models of sacrificing participants who could have conducted V2V transactions in order to prevent economic fraud. Meanwhile, the model ensures transactional economic benefits, transparency and fairness. This work facilitates EV adoption across the UK and globally, by increasing confidence and convenience in energy trading mechanisms.
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    Data-driven distributionally robust scheduling of community integrated energy systems with uncertain renewable generations considering integrated demand response
    (Elsevier, 2023-02-04) Li, Yang; Han, Meng; Shahidehpour, Mohammad; Li, Jiazheng; Long, Chao
    A community integrated energy system (CIES) is an important carrier of the energy internet and smart city in geographical and functional terms. Its emergence provides a new solution to the problems of energy utilization and environmental pollution. To coordinate the integrated demand response and uncertainty of renewable energy generation (RGs), a data-driven two-stage distributionally robust optimization (DRO) model is constructed. A comprehensive norm consisting of the 1-norm and ∞-norm is used as the uncertainty probability distribution information set, thereby avoiding complex probability density information. To address multiple uncertainties of RGs, a generative adversarial network based on the Wasserstein distance with gradient penalty is proposed to generate RG scenarios, which has wide applicability. To further tap the potential of the demand response, we take into account the ambiguity of human thermal comfort and the thermal inertia of buildings. Thus, an integrated demand response mechanism is developed that effectively promotes the consumption of renewable energy. The proposed method is simulated in an actual CIES in North China. In comparison with traditional stochastic programming and robust optimization, it is verified that the proposed DRO model properly balances the relationship between economical operation and robustness while exhibiting stronger adaptability. Furthermore, our approach outperforms other commonly used DRO methods with better operational economy, lower renewable power curtailment rate, and higher computational efficiency.
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    Framework design and optimal bidding strategy for ancillary service provision from a peer-to-peer energy trading community
    (Elsevier, 2020-08-28) Zhou, Yue; Wu, Jianzhong; Song, Guanyu; Long, Chao
    As an innovative paradigm for electric power systems with a high penetration of distributed energy resources, peer-to-peer (P2P) energy trading enables direct energy trading between end customers, which is able to facilitate local power and energy balance and potentially support the operation of bulk power systems. In this paper, a framework was proposed to enable ancillary service provision from a P2P energy trading community, creating additional value for both customers in the community and power systems. Specifically, an ancillary service provision mechanism was designed along with P2P energy trading and residual balancing mechanisms to enable the power utility to obtain ancillary service from customers in a P2P energy trading community. Furthermore, the optimal bidding strategy of customers was figured out to maximize their benefits in the proposed mechanisms. Simulation studies were conducted based on a residential community in Great Britain. The results show that the proposed ancillary service mechanism can enable the power utility to obtain a significant or required amount of ancillary services of different types. The proposed mechanisms and optimal bidding strategy can achieve Pareto improvement for the revenue of each customer and result in significantly higher social welfare for the whole community. It is also revealed that increasing ancillary service prices and installation rate of electric vehicles can increase the total amount of ancillary service provision and thus bring higher revenue for the customers in the community. By contrast, increasing installation of PV systems does not necessarily increase the amount of service provision
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    Integrated energy efficiency evaluation of a multi-source multi-load desalination micro-energy network
    (Elsevier, 2020-06-02) Wang, Dan; Yu, Jiancheng; Liu, Bo; Long, Chao; Chen, Peiyu; Chong, Zhiqiang
    With increasing global shortage of fresh water resources, many countries are prioritizing desalination as a means of utilizing abundantly available seawater resources. Integrated energy efficiency evaluation is a scientific method for the quantitative analysis of energy efficiency based on multiple indicators and is very useful for investment, construction, and scientific decision-making for desalination projects. In this paper, the energy efficiency evaluation of the micro energy network (MEN) of desalination for multi-source and multi-load is studied, and the basic idea of comprehensive energy efficiency evaluation is analyzed. The process includes the use of a MEN model to establish an integrated energy efficiency evaluation index system, taking into consideration energy, equipment, economic, environmental, and social factors. A combined evaluation method considering subjective and objective comprehensive weights for multi-source multi-load desalination MENs is proposed to evaluate the energy efficiency of desalination and from multiple perspectives
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    Optimal scheduling of island integrated energy systems considering multi-uncertainties and hydrothermal simultaneous transmission: a deep reinforcement learning approach
    (Elsevier, 2022-12-29) Li, Yang; Bu, Fanjin; Li, Yuanzheng; Long, Chao
    Multi-uncertainties from power sources and loads have brought significant challenges to the stable demand supply of various resources at islands. To address these challenges, a comprehensive scheduling framework is proposed by introducing a model-free deep reinforcement learning (DRL) approach based on modeling an island integrated energy system (IES). In response to the shortage of freshwater on islands, in addition to the introduction of seawater desalination systems, a transmission structure of “hydrothermal simultaneous transmission” (HST) is proposed. The essence of the IES scheduling problem is the optimal combination of each unit’s output, which is a typical timing control problem and conforms to the Markov decision-making solution framework of deep reinforcement learning. Deep reinforcement learning adapts to various changes and timely adjusts strategies through the interaction of agents and the environment, avoiding complicated modeling and prediction of multi-uncertainties. The simulation results show that the proposed scheduling framework properly handles multi-uncertainties from power sources and loads, achieves a stable demand supply for various resources, and has better performance than other real-time scheduling methods, especially in terms of computational efficiency. In addition, the HST model constitutes an active exploration to improve the utilization efficiency of island freshwater.
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    Residential community with PV and batteries: reserve provision under grid constraints
    (Elsevier, 2020-02-03) Alnaser, Sahban W.; Althaher, Sereen Z.; Long, Chao; Zhou, Yue; Wu, Jianzhong
    Technological advances in residential-scale batteries are paving the way towards self-sufficient communities to make the most use of their photovoltaic systems to support local energy consumption needs. To effectively utilize capabilities of batteries, the community can participate in the provision of short term operating reserve (STOR) services. To do so, adequate energy reserves in batteries are maintained during prescribed time windows to be utilized by electricity system operators. However, this may reduce energy sufficiency of the community. Further, the actual delivery of reserve could create distribution network congestions. To adequately understand the capability of a community to provide reserve, this work proposed a residential community energy management system formulated as a Mixed-Integer Linear Programming (MILP) model. This model aims to maximize energy sufficiency by optimal scheduling of batteries whilst considering reserve constraints. The model also maintains the aggregate power of houses within export/import limits that are defined offline using an iterative approach to ensure that the reserve provision does not breach distribution network constraints. The model is demonstrated on a residential community. The maximum committed reserve power with minimal impact on energy sufficiency is determined. Results also show that the capability of a community to provide reserve could be overestimated unless distribution network constraints are adequately considered.
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    Special issue on innovative methods and techniques for power and energy systems with high penetration of distributed energy resources [Editorial]
    (Elsevier, 2023-10-25) Li, Yang; Lei, Shunbo; Chen, Xia; Long, Chao; Zhou, Yifan; Kim, Young-Jin
    1. Background The contemporary landscape of power and energy systems (P&ESs) is experiencing a significant transformation, marked by the integration of distributed energy resources (DERs) like solar photovoltaics, wind turbines, energy storage systems, and electric vehicles. Although these DERs bring forth myriad benefits, they also introduce challenges in variability management, uncertainty, and cyber vulnerabilities. This special issue of Energy Reports offers a comprehensive perspective on these intertwined challenges and opportunities.
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    Systematic review of demand-side management strategies in power systems of developed and developing countries
    (MDPI, 2022-10-23) Usman, Rilwan; Mirzania, Pegah; Alnaser, Sahban W.; Hart, Phil; Long, Chao
    Balancing electricity demand and supply remains a significant challenge for the power systems in developing countries, such as Nigeria. In Nigeria, there is a shortage of adequate power supply, and demand-side management (DSM) plays a minor role in the power balancing mechanism with load shedding being widely used. The paper aims to review and compare various existing and emerging DSM strategies in developing countries. An extensive and systematic review was conducted to evaluate potential solutions using DSM to increase the overall energy efficiency in the Nigerian electricity market. This study found that, although the technical and economic potentials of DSM vary in developed countries, the uptakes of DSM have been severely hampered hence preventing the full exploitation and utilisation of the full potential of DSM. The initiatives of a DSM model in Nigeria and other developing countries can play a significant role in addressing demand and supply challenges but an upgrade of the energy infrastructures, a reform of the market structure and the provision of financial incentives are required to allow for wide implementations of DSM strategies in developing countries.
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    Transactive energy system for distribution network management: procuring residential flexibility services under dynamic pricing
    (IEEE, 2022-09-22) Althaher, Sereen Z.; Alnaser, Sahban W.; Zhou, Yue; Long, Chao
    The formulation of dynamic pricing is one of the emerging solutions to guide residential demand for the benefits of the bulk power system. However, the schedule of residential demand in response to time-differentiated energy prices could cause congestions in distribution networks at both the lowest-price and highest-price time intervals. To enable the adoption of dynamic pricing, this work presents a novel framework to manage the constraints of distribution networks based on the concept of Transactive Energy System (TES). The TES-based framework produces incentives during network issues to unlock customers’ flexibility services to reschedule controllable assets (e.g., batteries). By running Home Energy Management Systems (HEMS), the flexibility of customers to modify schedules are quantified against predefined set of incentives. For each incentive, the amounts of net-demand change per customer are aggregated and submitted through aggregators to the Distribution System Operator (DSO) in the forms of both generation offers (reducing demand) and demand offers (increasing demand). The latter are crucial to cater for generation-driven network issues. The resulting aggregators’ staircase bidding curves are embedded to an advanced Optimal Power Flow (OPF) model to identify the successful offers to manage network constraints whilst minimizing incentives paid to aggregators. This allows defining incentives and quantities directly without extensive iterations between DSO and aggregators. The application of the framework to an urban 11kV feeder shows its effectiveness to manage congestions. Further, the highly variations in dynamic prices increase the amounts of incentives particularly when flexibility services are requested at evening and night time intervals.
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    Transition towards solar Photovoltaic Self-Consumption policies with Batteries: from the perspective of distribution networks
    (Elsevier, 2021-10-05) Alnaser, Sahban W.; Althaher, Sereen Z.; Long, Chao; Zhou, Yue; Wu, Jianzhong; Hamdan, Reem
    The transition towards low-carbon energy systems requires increasing the contribution of residential Photovoltaic (PV) in the energy consumption needs (i.e., PV self-consumption). For this purpose, the adoption of PV self-consumption policies as alternatives to the current net-metering policy may support harnessing batteries to improve PV self-consumption. However, the technical impacts of PV policies on distribution networks have to be adequately assessed and mitigated. To do so, a two-stage planning framework is proposed. The first stage is an optimization approach that determines the best sizes of PV and batteries based on the adopted PV policy. The second stage assesses the impacts of the resulting sizes on distribution networks using Monte-Carlo simulations to cope with uncertainties in demand and generation. The framework is applied on real medium and low voltage distribution networks from the south of Jordan. For the net-metering, the results show that the uptake of residential PV penetration above 40% will result in voltage issues. It is also found that the adoption of batteries for the benefits of customers (i.e., reduce electricity bills) will not mitigate the PV impacts for PV penetration above 60%. Further, the results demonstrate the important role of distribution network operators to manage the uptake of batteries for the benefits of customers and distribution networks. Network operators can support customers to adopt larger sizes of batteries to achieve the desired PV self-consumption in return of controlling the batteries to solve network issues. This facilitates the uptake of 100% PV penetration and improves PV self-consumption to 50%.
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    Wind power forecasting considering data privacy protection: a federated deep reinforcement learning approach
    (Elsevier, 2022-11-16) Li, Yang; Wang, Ruinong; Li, Yuanzheng; Zhang, Meng; Long, Chao
    In a modern power system with an increasing proportion of renewable energy, wind power prediction is crucial to the arrangement of power grid dispatching plans due to the volatility of wind power. However, traditional centralized forecasting methods raise concerns regarding data privacy-preserving and data islands problem. To handle the data privacy and openness, we propose a forecasting scheme that combines federated learning and deep reinforcement learning (DRL) for ultra-short-term wind power forecasting, called federated deep reinforcement learning (FedDRL). Firstly, this paper uses the deep deterministic policy gradient (DDPG) algorithm as the basic forecasting model to improve prediction accuracy. Secondly, we integrate the DDPG forecasting model into the framework of federated learning. The designed FedDRL can obtain an accurate prediction model in a decentralized way by sharing model parameters instead of sharing private data which can avoid sensitive privacy issues. The simulation results show that the proposed FedDRL outperforms the traditional prediction methods in terms of forecasting accuracy. More importantly, while ensuring the forecasting performance, FedDRL can effectively protect the data privacy and relieve the communication pressure compared with the traditional centralized forecasting method. In addition, a simulation with different federated learning parameters is conducted to confirm the robustness of the proposed scheme.