Coupling the vehicle-to-grid (V2G) with integrated energy systems (IES) offers an emerging solution for decarbonisation of both energy and transport sectors. To evaluate the feasibility of coupling ...V2G with IES as a flexible storage, we propose an optimisation-based system planning framework embedding V2G into IES. Within this framework, stochastic features of electric vehicles (EV) fleets are simulated. The impacts of V2G on IES design are captured by assessing both economic and environmental benefits via multi-objective optimisations utilising an improved NSGA-II algorithm. Six case studies considering three cities with different climate conditions and two functional areas of residential and commercial are performed. The results manifest that Beijing-commercial case could achieve the largest mutual benefits. The EV fleets’ charging behaviour follows the time-of-use energy tariff in transition seasons while not during winter. Sensitivity analysis indicates the electricity and gas prices have significant impact on the system design. The benefits induced by growing EV penetration would gradually decrease and stabilise when the EV number reach 300, the growth of economic and environmental benefits stabilized at 1.3% and 1.8%, respectively. Overall, this study quantifies the benefits of enabling V2G in IES, and generates valuable insights for IES planners, V2G service providers, and relevant policymakers.
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•Co-optimisation of EV vehicle-to-grid (V2G) with integrated energy systems (IES).•Simulate stochastic features of EVs' charging patterns and initial battery states.•Capture V2G-IES design's trade-off by an efficient DM-NSGA-II algorithm.•Analyse parametric sensitivity of electricity price, gas price, and EV penetration.•V2G-IES achieves the largest mutual benefits in a Beijing-commercial demonstration.
Energy policy is too often not designed for energy consumers in a low-cost and consumer-friendly manner. This paper proposes a novel Stackelberg game and Blockchain-based framework that enables ...consumer-centric decarbonization by automating iterative negotiations between policy makers and consumers or generators to reduce carbon emissions. This iterative negotiation is modeled as a Stackelberg game-theoretic problem, and securely facilitated by Blockchain technologies. The policy maker formulates carbon prices and monetary compensation rates to dynamically incentivize the carbon reduction, whereas consumers and generators schedule their power profiles to minimize bills and maximize profits of generation, respectively. The negotiating agreement is yielded by reaching a Stackelberg equilibrium. The exchanged information and controlling functions are realized by using smart contracts of Blockchain technologies. Case studies of GB power systems show that the proposed framework can incentivize 9% more bill savings for consumers and 45.13% more energy generation from renewable energy sources. As a consumer-centric decarbonization framework, it can at least reduce carbon emissions by 40%.
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•A novel consumer-centric and negotiation-based framework to decarbonize power systems.•A Blockchain based platform to integrate the exchanged information and controlling functions of the power system scheduling into data structures, nodes, and smart contracts of Blockchain networks.•How Blockchain based platform can prevent cyber attacks is discussed.•Case studies demonstrate the effectiveness of the proposed framework in the context of Great Britain power systems.
•A novel Kriging surrogate enhanced Gate Recurrent Unit-Temporal Convolutional Network (GRU-TCN) model is proposed to derive an equivalent model for microgrid.•A novel PPF calculation method is ...designed based on the combination of the trained GRU-TCN deep learning model and mechanism-based power flow model.•The correlation between stochastic renewable energy power and multi-microgrids has been considered in detail for the PPF calculation.•A comprehensive comparative study is conducted to verify the effectiveness of the proposed method by comparing with the traditional analytical method and data-driven method.
With the massive deployment of microgrids (MGs) and energy communities, various stakeholders have been involved in distribution networks. Due to the underdeveloped information infrastructure, especially in rural distribution networks, there is an increasing number of “blind areas” in the operation of distribution networks. The calculation of probabilistic power flow (PPF) with incomplete parameters have become an urgent issue to be solved for ensuring safe operation. Based on the deep learning and mechanism models, a novel PPF method is proposed for multi-microgrids distribution systems considering incomplete network information. Firstly, accessible power exchange data as well as public and independent information are utilized to realize equivalent modeling for microgrids area with incomplete parameters, based on a novel Kriging surrogate enhanced Gate Recurrent Unit-Temporal Convolutional Network (GRU-TCN). Then, the PPF calculation is effectively conducted by the distribution system operator (DSO) through the point estimation method (PEM), in which the equivalent GRU-TCN models and model-based power flow are integrated. Therefore, the complicated interactive iteration of the power flow equation is avoided, and the PPF calculation efficiency is effectively improved. In addition, user privacy is protected because only the trained GRU-TCN deep learning models will be used by DSO for the PPF calculation. The proposed method is validated in a modified IEEE 33-node distribution network, a modified American PG&E 69-node distribution network as well as the modified three-phase unbalanced IEEE 123-node distribution network including several MGs with unknown internal network parameters. The results show that the proposed method can improve the PPF calculation efficiency greatly while ensuring high-precision calculation results. The required evaluation time can be reduced by 64.01% and 99.31% compared with the DNN-based Monte Carlo sampling method and the traditional mechanism model-based Monte Carlo sampling method with complete information, respectively.
Electric power systems are transitioning towards a decentralized paradigm with the engagement of active prosumers (both producers and consumers) through using distributed multi-energy sources. This ...paper proposes a novel Blockchain based peer-to-peer trading architecture which integrates negotiation-based auction and pricing mechanisms in local electricity markets, through automating, standardizing, and self-enforcing trading procedures using smart contracts. The negotiation of the volume and price of the peer-to-peer electricity trading among prosumers is modeled as a cooperative game, and the interaction between a retailer and its ensemble of prosumers is modeled as a Stackelberg game. The flexibility provision from residential heating systems is incorporated into the energy scheduling of prosumers. Case studies demonstrate that the proposed architecture in local electricity markets helps improve local energy balance. Flexibility from the residential heating systems enables prosumers to be more responsive to the variation of retail electricity prices. The proposed model reduces 41.24% of average daily electricity costs for individual prosumers or consumers compared to the case without the peer-to-peer electricity trading.
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•Enable supply and demand near-term technical and business model advances the LEO model.•Whole-system based LEO model covers local electricity, heating, building, transport ...sectors.•Assess two operational modes under two capital cost levels considering weather risks.•Battery storage and import power help local energy systems cope with dark and cold winter.•Heat pump, P2P energy trading, local PV are top three prioritized technologies for the local case.
On the way toward Net Zero 2050, the UK government set the 2035 target by slashing 78 % emissions compared to the 1990-level. To help understand how an electrified local energy system could contribute to this target and the associated cost, we develop a whole-system based local energy optimization (LEO) model. The model captures a series of state-of-the-art technologies including building fabric retrofit, battery storage, electro-mobility, electro-heating, demand response, distributed renewable, and Peer-to-Peer (P2P) energy trading. And the model enables trade-off assessment between cost and emissions minimization, compares two system operating modes, i.e., cost-oriented and grid-impact-oriented, and evaluates the impacts from weather risks and capital cost assumptions. A case study in Wales reveals (1) capital cost assumptions can lead up to 30.8 % overall cost difference of the local energy system; (2) operating the system in cost-oriented mode can save up to 5 % cost than in the grid-impact-oriented mode; (3) electro-heating by heat pumps has the highest priority among all investigated technologies. Overall, this study demonstrates how to design and operate a cost-efficient and electrified UK local energy system by the whole-system incorporation of near-term technical and business model advances towards a decarbonized future.
Low voltage distribution networks deliver power to the last mile of the network, but are often legacy assets from a time when low carbon technologies, e.g., electrified heat, storage, and electric ...vehicles, were not envisaged. Furthermore, exploiting emerging data from distribution networks to provide decision support for adapting planning and operational strategies with system transitions presents a challenge. To overcome these challenges, this paper proposes a novel application of digital twins based reinforcement learning to improve decision making by a distribution system operator, with key metrics of predictability, responsiveness, interoperability, and automation. The power system states, i.e., network configurations, technological combinations, and load patterns, are captured via a convolutional neural network, chosen for its pattern recognition capability with high-dimensional inputs. The convolutional neural networks are iteratively trained through the fitted Q-iteration algorithm, as a batch mode reinforcement learning, to adapt the planning and operational decisions with the dynamic system transitions. Case studies demonstrate the effectiveness of the proposed model by reducing 50% of the investment cost when the system transitions towards the winter and maintaining the power loss and loss of load within 5% compared to the benchmark optimisation. Doubled power consumption was observed in winter under future energy scenarios due to the electrification of heat. The trained model can accurately adapt optimal decisions according to the system changes while reducing the computational time of solving optimisation problems, for a range of scales of distribution systems, demonstrating its potential for scalable deployment by a system operator.
•Key features of network configurations, technology installations, and load patterns are digitally represented.•A novel digital twin-based distribution network model to adapt planning and operational decisions with dynamic state transitions.•Informed decisions to minimise the investment cost, power loss, loss of load, and renewable curtailment.•Synthesising scalable and computational efficient distribution networks.
In the transition to a society with net-zero carbon emissions, high penetration of distributed renewable power generation and large-scale electrification of transportation and heat are driving the ...conventional distribution network operators (DNOs) to evolve into distribution system operators (DSOs) that manage distribution networks in a more active and flexible way. As a radical decentralized data management technology, distributed ledger technology (DLT) has the potential to support a trustworthy digital infrastructure facilitating the DNO-DSO transition. Based on a comprehensive review of worldwide research and practice, as well as the engagement of relevant industrial experts, the application of DLT in distribution networks is identified and analyzed in this article. The DLT features and DSO needs are first summarized, and the mapping relationship between them is identified. Detailed DSO functions are identified and classified into five categories (i.e., "planning," "operation," "market," "asset," and "connection") with the potential of applying DLT to various DSO functions assessed. Finally, the development of seven key DSO functions with high DLT potential is analyzed and discussed from the technical, legal, and social perspectives, including peer-to-peer energy trading, flexibility market facilitation, electric vehicle charging, network pricing, distributed generation register, data access, and investment planning.
