| 1. |
- Najafi, Arsalan, 1987, et al.
(författare)
-
Coordination of coupled electrified road systems and active power distribution networks with flexibility integration
- 2024
-
Ingår i: Applied Energy. - 1872-9118 .- 0306-2619. ; 369
-
Tidskriftsartikel (refereegranskat)abstract
- Electric road systems (ERS) constitute a promising technology for mobile charging and relieving mandatory stops to recharge electric vehicles. However, the ERS operation is constrained by the limitations of the Power Distribution Network (PDN) that provides electricity. This study proposes a integrated optimization of a coupled ERS-PDN system (including traffic assignment and power flow modeling), in the presence of self-interested electric vehicle drivers, diverse flexibility resources and uncertainty of energy supplies (e.g. uncertainty from renewable energy). The security of the PDN while supporting ERS can be ensured by using active and flexible energy storage and flexible power loads. A semi-dynamic model is adopted for the traffic assignment. A stochastic bi-level optimization based on Stackelberg game under uncertainty is proposed to model the joint optimization problem to minimize the general cost of coupled ERS-PDN system and maximize the profit of the energy flexibility provider. Then, the Karush Kuhn Tucker conditions are deployed to convert the bi-level model to the equivalent single level model. The results demonstrate the effectiveness and benefits of the proposed framework using numerical experiments. The results show that the proposed optimization can reduce the burden of an ERS on the underlying PDN in improving the violated voltage by 3.66%, demonstrating the effect of joint consideration of diverse sources of flexibility.
|
|
| 2. |
- Cui, Shaohua, 1995, et al.
(författare)
-
Integration of UAVs with public transit for delivery: Quantifying system benefits and policy implications
- 2024
-
Ingår i: Transportation Research Part A: General. - 0965-8564. ; 183
-
Tidskriftsartikel (refereegranskat)abstract
- The maturation and scalability of unmanned aerial vehicle (UAV) technology offer transformative opportunities to revolutionize prompt delivery. This study explores integrating UAVs with public transportation vehicles (PTVs) to establish a novel delivery paradigm that enhances revenue for public transit operators and improves transport system efficiency without compromising passenger convenience or operational efficiency. Employing hexagonal planning technology, this study identifies and quantifies the available spatio-temporal resources of PTVs for UAV integration. This involves aligning the spatio-temporal dynamics of prompt delivery orders with PTV ridership, based on field data from Beijing's Haidian District. Utilizing these outputs, we quantitatively analyze the benefits of integrating UAVs with PTVs on increasing public transit revenue, and potentials of reducing carbon emissions and mitigating congestion. Furthermore, we quantify the long-term benefits of UAV-PTV integration by predicting future increases in delivery demand. Based on obtained quantitative results, this study discusses practical and policy implications to support the sustainable integration of UAVs with PTVs.
|
|
| 3. |
- Cui, Shaohua, 1995, et al.
(författare)
-
Joint optimal vehicle and recharging scheduling for mixed bus fleets under limited chargers
- 2023
-
Ingår i: Transportation Research Part E. - : Elsevier BV. - 1366-5545 .- 1878-5794. ; 180
-
Tidskriftsartikel (refereegranskat)abstract
- Owing to the high acquisition costs, maintenance expenses, and inadequate charging infrastructure associated with electric buses, achieving a complete replacement of diesel buses with electric counterparts in the short term proves challenging. A substantial number of bus operators currently find themselves in a situation where they must integrate electric buses with their existing diesel fleets. Confronted with the constraints of limited electric bus range and charging infrastructure, the primary concern for bus operators is how to effectively utilize their mixed bus fleets to adhere to pre-established bus timetables while maximizing the deployment of electric buses, known for their zero pollution and cost-effective travel. Consequently, this paper introduces the concept of the joint optimization problem for vehicle and recharging scheduling within mixed bus fleets operating under constrained charging conditions. To tackle this issue, a mixed integer linear model is formulated to optimize the coordination of bus schedules and recharging activities within the context of limited charging infrastructure. By establishing a set of feasible charging activities, the problem of electric buses queuing for charging at constrained charging stations is transformed into a linear optimization model constraint. Numerical simulations are conducted within the real transit network of the Dalian Economic Development Zone in China. The results indicate that the judicious joint optimization of vehicle and charging scheduling significantly enhances the service frequency of electric buses while reducing operational costs for bus lines. Notably, the proportion of total trips performed by electric buses rises to 80.4%.
|
|
| 4. |
- Fan, Jieyu, et al.
(författare)
-
Analyzing and Optimizing the Emission Impact of Intersection Signal Control in Mixed Traffic
- 2023
-
Ingår i: SUSTAINABILITY. - 2071-1050. ; 15:22
-
Tidskriftsartikel (refereegranskat)abstract
- Signalized intersections are one of the typical bottlenecks in urban transport systems that have reduced speeds and which have substantial vehicle emissions. This study aims to analyze and optimize the impacts of signal control on the emissions of mixed traffic flow (CO, HC, and NOx) containing both heavy- and light-duty vehicles at urban intersections, leveraging high-resolution field emission data. An OBEAS-3000 (Manufacturer: Xiamen Tongchuang Inspection Technology Co., Ltd., Xiamen, China.) vehicle emission testing device was used to collect microscopic operating characteristics and instantaneous emission data of different vehicle types (light- and heavy-duty vehicles) under different operating conditions. Based on the collected data, the VSP (Vehicle Specific Power) model combined with the VISSIM traffic simulation platform was used to quantitatively analyze the impact of signal control on traffic emissions. Heavy-duty vehicles contribute to most of the emissions regardless of the low proportion in the traffic flows. Afterward, a model is proposed for determining the optimal signal control at an intersection for a specific percentage of heavy-duty vehicles based on the conversion of emission factors of different types of vehicles. Signal control is also optimized based on conventional signal timing, and vehicle emissions are calculated. In the empirical analysis, the changes in CO, HC, and NOx emissions of light- and heavy-duty vehicles before and after conventional signal control optimization are quantified and compared. After the signal control optimization, the CO, HC, and NOx emissions of heavy-duty vehicles were reduced. The CO and HC emissions of light-duty vehicles were reduced, but the NOx emissions of light-duty vehicles remained unchanged. The emissions of vehicles after optimized signal control based on vehicle conversion factors are reduced more significantly than those after conventional optimized signal control. This study provides a scientific basis for developing traffic management measures for energy saving and emission reduction in transport systems with mixed traffic.
|
|
| 5. |
- Jasinski, Michal, et al.
(författare)
-
Microgrid working conditions identification based on cluster analysis – a case study from Lambda Microgrid
- 2022
-
Ingår i: IEEE Access. - 2169-3536 .- 2169-3536. ; 10, s. 70971-70979
-
Tidskriftsartikel (refereegranskat)abstract
- This article presents the application of cluster analysis (CA) to data proceeding from a testbed microgrid located at Sapienza University of Rome. The microgrid consists of photovoltaic (PV), battery storage system (BESS), emergency generator set, and different types of load with a real-time energy management system based on supervisory control and data acquisition. The investigation is based on the area-related approach - the CA algorithm considers the input database consisting of data from all measurement points simultaneously. Under the investigation, different distance measures (Euclidean, Chebyshev, or Manhattan), as well as an approach to the optimal number of cluster selections. Based on the investigation, the four different clusters that represent working conditions were obtained using methods to define an optimal number of clusters. Cluster 1 represented time with high PV production; cluster 2 represented time with relatively low PV production and when BESS was charged; cluster 3 represents time with relatively high PV production and when BESS was charged; cluster 4 represents time without PV production. Additionally, after the clustering process, a deep analysis was performed in relation to the working condition of the microgrid.
|
|
| 6. |
- Jasinski, Michal, et al.
(författare)
-
Operation and Planning of Energy Hubs Under Uncertainty - a Review of Mathematical Optimization Approaches
- 2023
-
Ingår i: IEEE Access. - 2169-3536 .- 2169-3536. ; 11, s. 7208-7228
-
Tidskriftsartikel (refereegranskat)abstract
- Co-designing energy systems across multiple energy carriers is increasingly attracting attention of researchers and policy makers, since it is a prominent means of increasing the overall efficiency of the energy sector. Special attention is attributed to the so-called energy hubs, i.e., clusters of energy communities featuring electricity, gas, heat, hydrogen, and also water generation and consumption facilities. Managing an energy hub entails dealing with multiple sources of uncertainty, such as renewable generation, energy demands, wholesale market prices, etc. Such uncertainties call for sophisticated decision-making techniques, with mathematical optimization being the predominant family of decision-making methods proposed in the literature of recent years. In this paper, we summarize, review, and categorize research studies that have applied mathematical optimization approaches towards making operational and planning decisions for energy hubs. Relevant methods include robust optimization, information gap decision theory, stochastic programming, and chance-constrained optimization. The results of the review indicate the increasing adoption of robust and, more recently, hybrid methods to deal with the multi-dimensional uncertainties of energy hubs.
|
|
| 7. |
- Kermani, Mostafa, 1986, et al.
(författare)
-
Optimal Self-scheduling of a real Energy Hub considering local DG units and Demand Response under Uncertainties
- 2021
-
Ingår i: IEEE Transactions on Industry Applications. - 0093-9994 .- 1939-9367. ; 57:4, s. 3396-3405
-
Tidskriftsartikel (refereegranskat)abstract
- In this paper, a cost-based mathematical optimization is used to evaluate the optimal amount of imported power from the public main grid to a private microgrid, that is the LAMBDA lab Microgrid testbed placed at Sapienza University of Rome. In this regard, this study considers five tests based on using different sources, including a photovoltaic array, an emergency generator set, a fuel cell and the main grid, for load satisfaction. The LAMBDA lab can be considered as a multi-source multi-output energy hub with three optional sources and both electrical and heat demands in output. This paper considers photovoltaic production and load demand as indeterministic parameters and evaluates the problem under uncertainties. As a result, a stochastic programming model is defined, and a powerful optimization function is used to reach the optimal power received from the main grid. In addition, information gap decision theory (IGDT) is used to model the robustness of the problem against uncertainties associated with renewable generation unit (Photovoltaic system) and electricity loads applied on a real case for the first time. In the result section, the contribution of each source in electrical and heat load demands is presented in addition to the cost of each test by evaluating the effect of DR of 15%. Finally, a comparison between the stochastic programming method and IGDT has been accomplished.
|
|
| 8. |
- Najafi, Arsalan, 1987, et al.
(författare)
-
A hybrid IGDT-robust optimization model for optimal self-scheduling of a smart home
- 2021
-
Ingår i: Conference Record - IAS Annual Meeting (IEEE Industry Applications Society). - 0197-2618. ; 2021-October
-
Konferensbidrag (refereegranskat)abstract
- This paper proposes a novel hybrid information gap decision theory (IGDT)-robust optimization (RO) model to solve the robust self-scheduling problem of a smart home (SH). This strategy gives a capability to the SH to manage its domestic energy production and consumption autonomously. The SH is fed by a wind turbine, a local market, and a battery. It is also allowed to sell/buy to/from a local market to reduce its cost or increase its profit. The SH supplies different types of load including controllable load, shiftable and non-shiftable loads. The electricity market prices and wind turbine generations are subject to uncertainty. Hence, the hybrid IGDT-RO framework is deployed to reach the worst-case realization of the electricity prices and wind turbine generations in the robust self-scheduling of the smart home. The results demonstrate that the optimal robust solutions are obtained with the proposed hybrid model and it makes sure the operator about the profitability of energy management.
|
|
| 9. |
- Yang, Ying, et al.
(författare)
-
Data-driven rolling eco-speed optimization for autonomous vehicles
- 2024
-
Ingår i: Frontiers of Engineering Management. - 2096-0255 .- 2095-7513. ; In Press
-
Tidskriftsartikel (refereegranskat)abstract
- In urban settings, fluctuating traffic conditions and closely spaced signalized intersections lead to frequent emergency acceleration, deceleration, and idling in vehicles. These maneuvers contribute to elevated energy use and emissions. Advances in vehicle-to-vehicle and vehicle-to-infrastructure communication technologies allow autonomous vehicles (AVs) to perceive signals over long distances and coordinate with other vehicles, thereby mitigating environmentally harmful maneuvers. This paper introduces a data-driven algorithm for rolling eco-speed optimization in AVs aimed at enhancing vehicle operation. The algorithm integrates a deep belief network with a back propagation neural network to formulate a traffic state perception mechanism for predicting feasible speed ranges. Fuel consumption data from the Argonne National Laboratory in the United States serves as the basis for establishing the quantitative correlation between the fuel consumption rate and speed. A spatiotemporal network is subsequently developed to achieve eco-speed optimization for AVs within the projected speed limits. The proposed algorithm results in a 12.2% reduction in energy consumption relative to standard driving practices, without a significant extension in travel time.
|
|
