Publications
547 results found
Fu P, Pudjianto D, Zhang X, et al., 2020, Integration of hydrogen into multi-energy systems optimisation, Energies, Vol: 13, Pages: 1606-1606, ISSN: 1996-1073
Hydrogen presents an attractive option to decarbonise the present energy system. Hydrogen can extend the usage of the existing gas infrastructure with low-cost energy storability and flexibility. Excess electricity generated by renewables can be converted into hydrogen. In this paper, a novel multi-energy systems optimisation model was proposed to maximise investment and operating synergy in the electricity, heating, and transport sectors, considering the integration of a hydrogen system to minimise the overall costs. The model considers two hydrogen production processes: (i) gas-to-gas (G2G) with carbon capture and storage (CCS), and (ii) power-to-gas (P2G). The proposed model was applied in a future Great Britain (GB) system. Through a comparison with the system without hydrogen, the results showed that the G2G process could reduce £3.9 bn/year, and that the P2G process could bring £2.1 bn/year in cost-savings under a 30 Mt carbon target. The results also demonstrate the system implications of the two hydrogen production processes on the investment and operation of other energy sectors. The G2G process can reduce the total power generation capacity from 71 GW to 53 GW, and the P2G process can promote the integration of wind power from 83 GW to 130 GW under a 30 Mt carbon target. The results also demonstrate the changes in the heating strategies driven by the different hydrogen production processes.
Jamieson MR, Strbac G, Bell KRW, 2020, Quantification and visualisation of extreme wind effects on transmission network outage probability and wind generation output, IET SMART GRID, Vol: 3, Pages: 112-122
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- Citations: 7
Qiu D, Ye Y, Papadaskalopoulos D, et al., 2020, A Deep Reinforcement Learning Method for Pricing Electric Vehicles with Discrete Charging Levels, IEEE Transactions on Industry Applications, ISSN: 0093-9994
The effective pricing of electric vehicles (EV) charging by aggregators constitutes a key problem towards the realization of the significant EV flexibility potential in deregulated electricity systems, and has been addressed by previous work through bi-level optimization formulations. However, the solution approach adopted in previous work cannot capture the discrete nature of the EV charging / discharging levels. Although reinforcement learning (RL) can tackle this challenge, state-of-the-art RL methods require discretization of state and / or action spaces and thus exhibit limitations in terms of solution optimality and computational requirements. This paper proposes a novel deep reinforcement learning (DRL) method to solve the examined EV pricing problem, combining deep deterministic policy gradient (DDPG) principles with a prioritized experience replay (PER) strategy, and setting up the problem in multi-dimensional continuous state and action spaces. Case studies demonstrate that the proposed method outperforms state-of-the-art RL methods in terms of both solution optimality and computational requirements, and comprehensively analyze the economic impacts of smart-charging and vehicle-to-grid (V2G) flexibility on both aggregators and EV owners.
Ye Y, Qiu D, Sun M, et al., 2020, Deep reinforcement learning for strategic bidding in electricity markets, IEEE Transactions on Smart Grid, Vol: 11, Pages: 1343-1355, ISSN: 1949-3053
Bi-level optimization and reinforcement learning (RL) constitute the state-of-the-art frameworks for modeling strategic bidding decisions in deregulated electricity markets. However, the former neglects the market participants' physical non-convex operating characteristics, while conventional RL methods require discretization of state and / or action spaces and thus suffer from the curse of dimensionality. This paper proposes a novel deep reinforcement learning (DRL) based methodology, combining a deep deterministic policy gradient (DDPG) method with a prioritized experience replay (PER) strategy. This approach sets up the problem in multi-dimensional continuous state and action spaces, enabling market participants to receive accurate feedback regarding the impact of their bidding decisions on the market clearing outcome, and devise more profitable bidding decisions by exploiting the entire action domain, also accounting for the effect of non-convex operating characteristics. Case studies demonstrate that the proposed methodology achieves a significantly higher profit than the alternative state-of-the-art methods, and exhibits a more favourable computational performance than benchmark RL methods due to the employment of the PER strategy.
Huyghues-Beaufond N, Tindemans S, Falugi P, et al., 2020, Robust and automatic data cleansing method for short-term load forecasting of distribution feeders, Applied Energy, Vol: 261, Pages: 1-17, ISSN: 0306-2619
Distribution networks are undergoing fundamental changes at medium voltage level. To support growing planning and control decision-making, the need for large numbers of short-term load forecasts has emerged. Data-driven modelling of medium voltage feeders can be affected by (1) data quality issues, namely, large gross errors and missing observations (2) the presence of structural breaks in the data due to occasional network reconfiguration and load transfers. The present work investigates and reports on the effects of advanced data cleansing techniques on forecast accuracy. A hybrid framework to detect and remove outliers in large datasets is proposed; this automatic procedure combines the Tukey labelling rule and the binary segmentation algorithm to cleanse data more efficiently, it is fast and easy to implement. Various approaches for missing value imputation are investigated, including unconditional mean, Hot Deck via k-nearest neighbour and Kalman smoothing. A combination of the automatic detection/removal of outliers and the imputation methods mentioned above are implemented to cleanse time series of 342 medium-voltage feeders. A nested rolling-origin-validation technique is used to evaluate the feed-forward deep neural network models. The proposed data cleansing framework efficiently removes outliers from the data, and the accuracy of forecasts is improved. It is found that Hot Deck (k-NN) imputation performs best in balancing the bias-variance trade-off for short-term forecasting.
Heylen E, Papadaskalopoulos D, Konstantelos I, et al., 2020, Dynamic modelling of consumers’ inconvenience associated with demand flexibility potentials, Sustainable Energy, Grids and Networks, Vol: 21, Pages: 1-13, ISSN: 2352-4677
Demand flexibility, involving the potential to reduce or temporally defer electricity demand, is regarded as a key enabler for transitioning to a secure, cost-efficient and low-carbon energy future. However, previous work has not comprehensively modelled the inconvenience experienced by end-consumers due to demand modifications, since it has focused on static modelling approaches. This paper presents a novel model of inconvenience cost that simultaneously accounts for differentiated preferences of consumer groups, time and duration of interruptions, differentiated valuation of different units of power and temporal redistribution of shiftable loads. This model is dynamic and future-agnostic, implying that it captures the time-coupling characteristics of consumers’ flexibility and the temporal evolution of interruptions, without resorting to the unrealistic assumption that time and duration of interruptions are foreknown. The model is quantitatively informed by publicly available surveys combined with realistic assumptions and suitable sensitivity analyses regarding aspects excluded from existing surveys. In the examined case studies, the developed model is applied to manage an aggregator’s portfolio in a scenario involving emergence of an adequacy issue in the Belgian system. The results illustrate how considering each of the above factors affects demand management decisions and the inconvenience cost, revealing the value of the developed model.
Soder L, Tomasson E, Estanqueiro A, et al., 2020, Review of wind generation within adequacy calculations and capacity markets for different power systems, Renewable and Sustainable Energy Reviews, Vol: 119, Pages: 1-15, ISSN: 1364-0321
The integration of renewable energy sources, including wind power, in the adequacy assessment of electricity generation capacity becomes increasingly important as renewable energy generation increases in volume and replaces conventional power plants. The contribution of wind power to cover the electricity demand is less certain than conventional power sources; therefore, the capacity value of wind power is smaller than that of conventional plants.This article presents an overview of the adequacy challenge, how wind power is handled in the regulation of capacity adequacy, and how wind power is treated in a selection of jurisdictions. The jurisdictions included in the overview are Sweden, Great Britain, France, Ireland, United States (PJM and ERCOT), Finland, Portugal, Spain, Norway, Denmark, Belgium, Germany, Italy and the Netherlands.
Malekpour M, Azizipanah-Abarghooee R, Teng F, et al., 2020, Fast Frequency Response From Smart Induction Motor Variable Speed Drives, IEEE TRANSACTIONS ON POWER SYSTEMS, Vol: 35, Pages: 997-1008, ISSN: 0885-8950
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- Citations: 15
Ye Y, Qiu D, Wu X, et al., 2020, Model-Free Real-Time Autonomous Control for A Residential Multi-Energy System Using Deep Reinforcement Learning, IEEE Transactions on Smart Grid, Pages: 1-1, ISSN: 1949-3053
Multi-energy systems (MES) are attracting increasing attention driven by its potential to offer significant flexibility in future smart grids. At the residential level, the roll-out of smart meters and rapid deployment of smart energy devices call for autonomous multi-energy management systems which can exploit real-time information to optimally schedule the usage of different devices with the aim of minimizing end-users’ energy costs. This paper proposes a novel real-time autonomous energy management strategy for a residential MES using a model-free deep reinforcement learning (DRL) based approach, combining state-of-the-art deep deterministic policy gradient (DDPG) method with an innovative prioritized experience replay strategy. This approach is tailored to align with the nature of the problem by posing it in multi-dimensional continuous state and action spaces, facilitating more cost-effective control strategies to be devised. The superior performance of the proposed approach in reducing end-user’s energy cost while coping with the MES uncertainties is demonstrated by comparing it against state-of-the-art DRL methods as well as conventional stochastic programming and robust optimization methods in numerous case studies in a real-world scenario.
Ye Y, Qiu D, Wu X, et al., 2020, Model-Free Real-Time Autonomous Control for A Residential Multi-Energy System Using Deep Reinforcement Learning, IEEE Transactions on Smart Grid, Vol: 11, Pages: 3068-3068, ISSN: 1949-3053
Multi-energy systems (MES) are attracting increasing attention driven by its potential to offer significant flexibility in future smart grids. At the residential level, the roll-out of smart meters and rapid deployment of smart energy devices call for autonomous multi-energy management systems which can exploit real-time information to optimally schedule the usage of different devices with the aim of minimizing end-users’ energy costs. This paper proposes a novel real-time autonomous energy management strategy for a residential MES using a model-free deep reinforcement learning (DRL) based approach, combining state-of-the-art deep deterministic policy gradient (DDPG) method with an innovative prioritized experience replay strategy. This approach is tailored to align with the nature of the problem by posing it in multi-dimensional continuous state and action spaces, facilitating more cost-effective control strategies to be devised. The superior performance of the proposed approach in reducing end-user’s energy cost while coping with the MES uncertainties is demonstrated by comparing it against state-of-the-art DRL methods as well as conventional stochastic programming and robust optimization methods in numerous case studies in a real-world scenario.
Li J, Ye Y, Strbac G, 2020, Incentivizing Peer-to-Peer Energy Sharing Using a Core Tâtonnement Algorithm, 2020 IEEE IEEE Power & Energy Society General Meeting
Georgiou S, Aunedi M, Strbac G, et al., 2020, On the value of liquid-air and Pumped-Thermal Electricity Storage systems in low-carbon electricity systems, Energy, Vol: 193, ISSN: 0360-5442
We consider two medium-to-large scale thermomechanical electricity storage technologies currently under development, namely ‘Liquid-Air Energy Storage’ (LAES) and ‘Pumped-Thermal Electricity Storage’ (PTES). Consistent thermodynamic models and costing methods based on a unified methodology for the two systems from previous work are presented and used with the objective of integrating the characteristics of the technologies into a whole-electricity system assessment model and assessing their system-level value in various scenarios for system decarbonization. It is found that the value of storage depends on the cumulative installed capacity of storage in the system, with storage technologies providing greater marginal benefits at low penetrations. The system value of PTES was found to be slightly higher than that of LAES, driven by a higher storage duration and efficiency, although these results must be seen in light of the uncertainty in the (as yet, not demonstrated) performance of key PTES components, namely the reciprocating-piston compressors and expanders. At the same time, PTES was also found to have a higher power capital cost. The results indicate that the complexity of the decarbonization challenge makes it difficult to identify clearly a ‘best’ technology and suggest that the uptake of either technology can provide significant system-level benefits.
Badesa L, Teng F, Strbac G, 2020, Pricing inertia and Frequency Response with diverse dynamics in a Mixed-Integer Second-Order Cone Programming formulation, Applied Energy, Vol: 260, Pages: 1-11, ISSN: 0306-2619
Low levels of system inertia in power grids with significant penetration of non-synchronous Renewable Energy Sources (RES) have increased the risk of frequency instability. The provision of a certain type of ancillary services such as inertia and Frequency Response (FR) is needed at all times, to maintain system frequency within secure limits if the loss of a large power infeed were to occur. In this paper we propose a frequency-secured optimisation framework for the procurement of inertia and FR with diverse dynamics, which enables to apply a marginal-pricing scheme for these services. This pricing scheme, deduced from a Mixed-Integer Second-Order Cone Program (MISOCP) formulation that represents frequency-security constraints, allows for the first time to appropriately value multi-speed FR.
Giannelos S, Djapic P, Pudjianto D, et al., 2020, Quantification of the energy storage contribution to security of supply through the F-factor methodology, Energies, Vol: 13, Pages: 826-826, ISSN: 1996-1073
The ongoing electrification of the heat and transport sectors is expected to lead to a substantial increase in peak electricity demand over the coming decades, which may drive significant investment in network reinforcement in order to maintain a secure supply of electricity to consumers. The traditional way of security provision has been based on conventional investments such as the upgrade of the capacity of electricity transmission or distribution lines. However, energy storage can also provide security of supply. In this context, the current paper presents a methodology for the quantification of the security contribution of energy storage, based on the use of mathematical optimization for the calculation of the F-factor metric, which reflects the optimal amount of peak demand reduction that can be achieved as compared to the power capability of the corresponding energy storage asset. In this context, case studies underline that the F-factors decrease with greater storage power capability and increase with greater storage efficiency and energy capacity as well as peakiness of the load profile. Furthermore, it is shown that increased investment in energy storage per system bus does not increase the overall contribution to security of supply.
Song Y, Ding Y, Siano P, et al., 2020, Optimization methods and advanced applications for smart energy systems considering grid-interactive demand response, APPLIED ENERGY, Vol: 259, ISSN: 0306-2619
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- Citations: 8
Cremer JL, Konstantelos I, Strbac G, 2020, Optimized operation rules for imbalanced classes, 2019 IEEE Power & Energy Society General Meeting (PESGM), Publisher: IEEE, Pages: 1-5
Supervised machine learning methods were applied to assess the reliability of the power system. Typically, the reliability boundary that defines the operation rules is learned using a training database consisting of a large number of potential operation states. Many of these operation states are historical observations and these are typically all reliable operation states. However, to learn a classifier that can predict unseen operation states requires unreliable operation states as well. Thus, a statistical model is typically fitted to the historical observations, and then, unreliable operation states are sampled from this model. Still, the share of reliable states may be much larger than the portion of unreliable states. This imbalance in the data results in biasing the learning methods toward predicting reliable states with higher accuracy than unreliable states. However, an unreliable operating state involves (per-definition) a risk of failing system operation. Therefore, a higher accuracy is required in predicting unreliable states rather than in reliable states. This paper focuses on accounting for this bias when learning from imbalanced data. To optimally learn operation rules for an imbalanced training database a novel Optimal Classification Tree (OCT) is applied. We modify the OCT approach to address the corresponding bias that is introduced in an imbalanced training database. Our fully Controllable and Optimal Classification Tree (COCT) approach controls directly in the objective function the class weights of each operation state that is used for training. By using a database from the French transmission grid it is showcased how the proposed COCT method results in fewer missed alarms than the standard approach that is used to learn operation rules.
Qiu D, Papadaskalopoulos D, Ye Y, et al., 2020, Investigating the Effects of Demand Flexibility on Electricity Retailers’ Business through a Tri-Level Optimization Model, IET Generation, Transmission & Distribution, Vol: 14, Pages: 1739-1750, ISSN: 1751-8687
The investigation of the effects of demand flexibility on the pricing strategies and the profits of electricity retailers has recently emerged as a highly interesting research area. However, the state-of-the-art, bi-level optimization modelling approach makes the unrealistic assumption that retailers treat wholesale market prices as exogenous, fixed parameters. This paper proposes a tri-level optimization model which drops this assumption and represents endogenously the wholesale market clearing process, thus capturing the realistic implications of a retailer’s pricing strategies and the resulting demand response on the wholesale market prices. The scope of the examined case studies is threefold. First of all, they demonstrate the interactions between the retailer, the flexible consumers and the wholesale market and analyse the fundamental effects of the consumers’ time-shifting flexibility on the retailer’s revenue from the consumers, its cost in the wholesale market, and its overall profit. Furthermore, they analyse how these effects of demand flexibility depend on the retailer’s relative size in the market and the strictness of the regulatory framework. Finally, they highlight the added value of the proposed tri-level model by comparing its outcomes against the state-of-the-art bi-level modelling approach.
Ye Y, Papadaskalopoulos D, Kazempour J, et al., 2020, Incorporating non-convex operating characteristics into bi-level optimization electricity market models, IEEE Transactions on Power Systems, Vol: 35, Pages: 163-176, ISSN: 0885-8950
Bi-level optimization constitutes the most popular mathematical methodology for modeling the deregulated electricity market. However, state-of-the-art models neglect the physical non-convex operating characteristics of market participants, due to their inherent inability to capture binary decision variables in their representation of the market clearing process, rendering them problematic in modeling markets with complex bidding and unit commitment (UC) clearing mechanisms. This paper addresses this fundamental limitation by proposing a novel modeling approach enabling incorporation of these non-convexities into bi-level optimization market models, which is based on the relaxation and primal-dual reformulation of the original, non-convex lower level problem and the penalization of the associated duality gap. Case studies demonstrate the ability of the proposed approach to closely approximate the market clearing solution of the actual UC clearing algorithm and devise more profitable bidding decisions for strategic producers than the state-of-the-art bi-level optimization approach, and reveal the potential of strategic behavior in terms of misreporting non-convex operating characteristics.
Greenwood DM, Djapic P, Sarantakos I, et al., 2020, Pragmatic method for assessing the security of supply in future smart distribution networks, Pages: 221-224
Future distribution networks will be able to provide security of supply through a combination of conventional and smart solutions. This has the potential to require complex and time-consuming assessments using cost-benefit analysis and probabilistic, risk-based methods. The key goal of this project is to create a method for evaluating the security of supply from a combination of conventional and smart solutions which is rigorous enough to provide robust answers, but simple enough that it can be used routinely by planning engineers without in-depth knowledge of risk, statistics, probability, or reliability theory. This will be accomplished through an iterative, data-driven approach and validated via established risk analysis methods. This study presents underpinning analysis for the development of that method, including in-depth risk studies and sensitivity analysis of real distribution networks.
Papadaskalopoulos D, Fan Y, De Paola A, et al., 2020, Game-theoretic modeling of merchant transmission investments, Lecture Notes in Energy, Pages: 381-414
Merchant transmission investment planning has recently emerged as a promising alternative or complement to the traditional centralized planning paradigm and it is considered as a further step toward the deregulation and liberalization of the electricity industry. However, its widespread application requires addressing two fundamental research questions: which entities are likely to undertake merchant transmission investments and whether this planning paradigm can maximize social welfare as the traditional centralized paradigm. Unfortunately, previously proposed approaches to quantitatively model this new planning paradigm do not comprehensively capture the strategic behavior and decision-making interactions between multiple merchant investors. This Chapter proposes a novel non-cooperative game-theoretic modeling framework to capture these realistic aspects of merchant transmission investments and provide insightful answers to the above research questions. More specifically, two different models, both based on non-cooperative game theory, have been developed. The first model addresses the first research question by adopting an equilibrium programming approach. The decision-making problem of each merchant investing player is formulated as a bi-level optimization problem, accounting for the impacts of its own actions on locational marginal prices (LMP) as well as the actions of all competing players. This problem is solved after converting it to a mathematical program with equilibrium constraints (MPEC). An iterative diagonalization method is employed to search for the likely outcome of the strategic interactions between multiple players, i.e., Nash equilibria (NE) of the game. Case studies on a simple 2-node system demonstrate that merchant networks investments will be mostly undertaken by generation companies in areas with low LMP and demand companies in areas with high LMP, as apart from collecting congestion revenue they also increase their energy surpluses. These
Oulis Rousis A, Konstantelos I, Strbac G, 2020, A Planning Model for a Hybrid AC–DC Microgrid Using a Novel GA/AC OPF Algorithm, IEEE Transactions on Power Systems, Vol: 35, Pages: 227-237, ISSN: 0885-8950
This paper focuses on developing an appropriate combinatorial optimization technique for solving the optimal sizing problem of hybrid AC/DC microgrids. A novel two-stage iterative approach is proposed. In the first stage, a metaheuristic technique based on a tailor-made genetic algorithm is used to tackle the optimal sizing problem, while, in the second, a non-linear solver is deployed to solve the operational problem subject to the obtained design/investment decisions. The proposed approach, being able to capture technical characteristics such as voltage and frequency through a detailed power flow algorithm, provides accurate solutions and therefore can address operational challenges of microgrids. Its capability to additionally capture contingencies ensures that the proposed sizing solutions are suitable both during normal operation and transient states. Finally, the genetic algorithm provides convergence of the model with relative computational simplicity. The proposed model is applied to a generalizable microgrid comprising of AC and DC generators and loads, as well as various types of storage technologies in order to demonstrate the benefits. The load and natural resources data correspond to real data.
Sun M, Zhang T, Wang Y, et al., 2020, Using Bayesian deep learning to capture uncertainty for residential net load forecasting, IEEE Transactions on Power Systems, Vol: 35, Pages: 188-201, ISSN: 0885-8950
Decarbonization of electricity systems drives significant and continued investments in distributed energy sources to support the cost-effective transition to low-carbon energy systems. However, the rapid integration of distributed photovoltaic (PV) generation presents great challenges in obtaining reliable and secure grid operations because of its limited visibility and intermittent nature. Under this reality, net load forecasting is facing unprecedented difficulty in answering the following question: how can we accurately predict the net load while capturing the massive uncertainties arising from distributed PV generation and load, especially in the context of high PV penetration? This paper proposes a novel probabilistic day-ahead net load forecasting method to capture both epistemic uncertainty and aleatoric uncertainty using Bayesian deep learning, which is a new field that combines Bayesian probability theory and deep learning. The proposed methodological framework employs clustering in subprofiles and considers residential rooftop PV outputs as input features to enhance the performance of aggregated net load forecasting. Numerical experiments have been carried out based on fine-grained smart meter data from the Australian grid with separately recorded measurements of rooftop PV generation and loads. The results demonstrate the superior performance of the proposed scheme compared with a series of state-of-theart methods and indicate the importance and effectiveness of subprofile clustering and high PV visibility.
Meenakumar P, Aunedi M, Strbac G, 2020, Optimal Business Case for Provision of Grid Services through EVs with V2G Capabilities, 15th International Conference on Ecological Vehicles and Renewable Energies (EVER), Publisher: IEEE
Aunedi M, Strbac G, 2020, Whole-system Benefits of Vehicle-to-Grid Services from Electric Vehicle Fleets, 15th International Conference on Ecological Vehicles and Renewable Energies (EVER), Publisher: IEEE
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- Citations: 2
Wang Y, Rousis AO, Strbac G, 2020, Resilience-Driven Modeling, Operation and Assessment for a Hybrid AC/DC Microgrid, IEEE ACCESS, Vol: 8, Pages: 139756-139770, ISSN: 2169-3536
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- Citations: 11
Ye Y, Qiu D, Sun M, et al., 2020, Deep Reinforcement Learning for Strategic Bidding in Electricity Markets, IEEE-Power-and-Energy-Society General Meeting (PESGM), Publisher: IEEE, ISSN: 1944-9925
Ye Y, Papadaskalopoulos D, Kazempour J, et al., 2020, Incorporating Non-Convex Operating Characteristics into Bi-Level Optimization Electricity Market Models, IEEE-Power-and-Energy-Society General Meeting (PESGM), Publisher: IEEE, ISSN: 1944-9925
Pudjianto D, Djapic P, Strbac G, et al., 2020, DER reactive services and distribution network losses, CIRED 2020 Berlin Workshop (CIRED 2020), Publisher: Institution of Engineering and Technology (IET), Pages: 541-544, ISSN: 2515-0855
Managing synergies and conflicts between voltage support services and network losses is essential for the cost-effective integration of distributed energy resources (DERs). This study presents the results of studies investigating the impact of using DER reactive power services on distribution network losses. By using year-round optimal power flow analysis, a spectrum of studies on a number of distribution network areas in the southeast of Great Britain was performed to calculate distribution losses under different control scenarios. The studies demonstrate that the use of DERs to provide reactive services to the transmission system may increase distribution network losses. On the other hand, DER reactive services can also be optimised to minimise distribution losses. The studies also analysed the impact of optimising tap changing transformer settings on the distribution network losses reduction.
O'Malley C, Aunedi M, Teng F, et al., 2020, Value of Fleet Vehicle to Grid in Providing Transmission System Operator Services, 15th International Conference on Ecological Vehicles and Renewable Energies (EVER), Publisher: IEEE
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- Citations: 1
Wu Z, Guo F, Polak J, et al., 2019, Evaluating grid-interactive electric bus operation and demand response with load management tariff, Applied Energy, Vol: 255, Pages: 1-12, ISSN: 0306-2619
Electric Vehicles are expected to play a vital role in the transition of smart energy systems. Lots of recent research has explored numerous underlying mechanisms to achieve the synergetic interactions in the electricity balancing process. In this paper, the grid-interactive operation of electric buses is first time integrated within a dynamic market frame using the Distribution Locational Marginal Price algorithm for load congestion management. Since the defined problem correlates the opportunity charging flexibility with the bus mobility over a network, the tempo-spatial distribution of energy needs can be reflected in the dynamic of service planning. The interactions between bus operators and suppliers are quantitatively modelled by a bi-level optimisation process to represent the electric bus service planning and electricity market clearing separately. The effectiveness of the proposed load management has been demonstrated using data collected from an integrated real-world bus network. Experiments show that engagement of electric bus charging load in demand response is helpful to alleviate the network congestion and to reduce the power loss by 7.2% in the distribution network. However, alleviated charging loads have exhibited counter-intuitive ability for load shifting. The restricted electric bus operational requirements leads to a 8.17% loss of charging demand, while the reliance on large batteries has increased by 10.57%. However, the sensitivity analysis also shows that as the battery cost declines, the such discourage implications on grid-interactive electric bus operation will decrease once the battery cost below 190/kWh. The optimal grid-ebus integration have to consider the trade-off between range add-up, reduced battery cost and additional benefits.
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