In order to cope with global climate change, an electric vehicle (EV) and new energy building are constantly being innovated and improved. With the popularity and application of big data and Internet of Things, the ne...
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In order to cope with global climate change, an electric vehicle (EV) and new energy building are constantly being innovated and improved. With the popularity and application of big data and Internet of Things, the new energy building with available charging piles may also become a charging station, which can solve the problem of difficult charging of EVs and promote the local energy consumption of the building. Therefore, this study proposes a shared charging concept for buildings, that is, shared photovoltaic, charging, and energy storage building (sPCEB). First, based on the analysis results of big data in cities or settlements of people, a locating method of the sPCEB system is introduced, and further proposes an optimal operating strategy that maximizes the combined benefit of the building. The efficiency and effectiveness of the proposed methods are verified by simulation.
This paper proposes a new optimization model based on mixed-integer linear programming approach for sizing a solar-windgrid-connected system. The proposed hybrid system aims to supply load demand for an industrial fac...
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This paper proposes a new optimization model based on mixed-integer linear programming approach for sizing a solar-windgrid-connected system. The proposed hybrid system aims to supply load demand for an industrial facility in Saudi Arabia. The developed model determines the optimal number of photovoltaic modules and wind turbines, as well as the optimal hourly energy obtained from the grid and hourly excess energy produced by the system and sold to the grid. The model findings show that 77 photovoltaic modules and 7 wind turbines in addition to the grid are needed to satisfy the load demand. The system produces 450,734 kWh annually which represents 82% of the annual load consumption. Also, the system produces surplus energy of 72,752 kWh which accounts to $6,184 as an income to the industrial facility. The case study is enriched by considering off-grid photovoltaic-wind system with battery storage. The results show that incorporating battery storage increases the cost of energy of the hybrid system. However, the off-grid photovoltaic-wind system shows promising results when considering environmental issues as it will displace around of 279,800 kg of carbon dioxide per year compared to the grid-connected system that will displace around 225,768 kg of carbon dioxide per year. A sensitivity analysis was carried out to identify the effect of the electricity cost on the optimal system design and the cost of energy obtained from the hybrid system.
The high penetration of Distributed Energy Resources (DERs) into the demand side has led to an increase in the number of consumers becoming prosumers. Recently, Peer-to-Peer (P2P) energy trading has gained increased p...
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The high penetration of Distributed Energy Resources (DERs) into the demand side has led to an increase in the number of consumers becoming prosumers. Recently, Peer-to-Peer (P2P) energy trading has gained increased popularity as it is considered an effective approach for managing DERs and offering local market solutions. This paper presents a P2P Energy Management System (EMS) that aims to reduce the absolute net energy exchange with the utility by exploiting two days-ahead energy forecast and allowing the exchange of the surplus energy among prosumers. mixed-integer linear programming (MILP) is used to schedule the day-ahead household battery energy exchange with the utility and other prosumers. The proposed system is tested using the measured data for a community of six houses located in London, UK. The proposed P2P EMS enhanced the energy independency of the community by reducing the exchanged energy with the utility. The results show that the proposed P2P EMS reduced the household operating costs by up to 18.8% when it is operated as part of the community over four months compared to operating individually. In addition, it reduced the community's total absolute net energy exchange with the utility by nearly 25.4% compared to a previous state-of-the-art energy management method.
The technological advancements involving information and communication technologies (ICT), such as Connected and Automated Vehicles (CAVs) and the Intelligent Transport Systems (ITS), have enabled new efficient traffi...
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The technological advancements involving information and communication technologies (ICT), such as Connected and Automated Vehicles (CAVs) and the Intelligent Transport Systems (ITS), have enabled new efficient traffic control and management strategies to mitigate traffic congestion. Specifically, the combined traffic flow-speed advisory systems based on CAVs and ITS technologies could provide the individual vehicle with the optimal speed to reduce the fuel consumption, the number of stops, simultaneously reduce the network-wide traffic congestion and improve road safety. This article develops a novel bi-level control framework underpinned by the mutual interaction between a system optimal traffic flow control strategy at a network level and a speed control policy for an individual vehicle at a link level within a connected traffic environment. Our framework proposes the novel group-based method to guarantee the consistency and interaction between the macroscopic and microscopic models. To this end, it efficiently optimizes vehicular trajectories while meeting the network-wide objectives which have not been investigated previously in the literature. We propose an efficient algorithm for this problem that iteratively solves mixed-integer linear programming (MILP) models for each upper and lower level. Numerical results indicate the effectiveness of the proposed speed advisory method in vehicular emission reduction, favorable network queue formation, and its positive influence on traffic flow patterns over the network.
Bad data identification is an essential prerequisite for state estimation in distribution networks. When multiple leverage measurements with bad data occur in the system, traditional residual-based methods are ineffec...
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In the Era of Industry 4.0, the shift from physical infrastructure to cloud data centers has become a growing trend among companies and enterprises of various scales. However, the growth of modern cloud data centers h...
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In this paper, we address mixed-integerlinear bilevel optimization problems. In bilevel optimization, a (lower-level) optimization problem is included in the constraints of another (upper-level) optimization problem....
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ISBN:
(纸本)9783031464393;9783031464386
In this paper, we address mixed-integerlinear bilevel optimization problems. In bilevel optimization, a (lower-level) optimization problem is included in the constraints of another (upper-level) optimization problem. Thus, this framework is especially adequate to model hierarchical decision processes. We analyze a state-of-the-art algorithm developed for this type of problems, which is based on an optimal-value-function reformulation and consists of an iterative deterministic bounding procedure. Computational experiments are made with data instances with different characteristics. The performance of the algorithm in the different groups of problems is discussed.
The aim of distribution networks is to meet their local area power demand with maximum reliability. As the electricity consumption tends to increase every year, limited line thermal capacity can lead to network conges...
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ISBN:
(纸本)9798350315097
The aim of distribution networks is to meet their local area power demand with maximum reliability. As the electricity consumption tends to increase every year, limited line thermal capacity can lead to network congestion. Continuous development and upgradation of the distribution network is thus required to meet the energy demand, which poses a significant increase in cost. The objective of this research is to analyze distribution network topologies and introduce a topology reconfiguration scheme based on the cost and demand of electricity. Traditional electrical distribution networks are static and inefficient. To make the network active, an optimal dynamic network topology reconfiguration (DNTR) is proposed to control line switching and reconnect some loads to different substations such that the cost of electricity can be minimized. The proposed DNTR strategy was tested on a synthetic radial distribution network with three substations each connecting to an IEEE 13-bus system. Simulation results demonstrated significant cost saving in daily operations of this distribution system.
Power restoration is an urgent task after a blackout, and recovery efficiency is critical when quantifying system resilience. Multiple elements should be considered to restore the power system quickly and safely. This...
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ISBN:
(纸本)9781665464413
Power restoration is an urgent task after a blackout, and recovery efficiency is critical when quantifying system resilience. Multiple elements should be considered to restore the power system quickly and safely. This paper proposes a recovery model to solve a direct-current optimal power flow (MOPE) based on mixed-integer linear programming (MILP). Since most of the generators cannot start independently, the interaction between black-start (BS) and non-black-start (NBS) generators must be modeled appropriately. The energization status of the NBS is coordinated with the recovery status of transmission lines, and both of them are modeled as binary variables. Also, only after an NBS unit receives cranking power through connected transmission lines, will it be allowed to participate in the following system dispatch. The amount of cranking power is estimated as a fixed proportion of the maximum generation capacity. The proposed model is validated on several test systems, as well as a 1393-bus representation system of the Puerto Rican electric power grid. Test results demonstrate how the recovery of NBS units and damaged transmission lines can he optimized, resulting in an efficient and well-coordinated recovery procedure.
The fragmentation of wildlife habitats caused by anthropogenic activities has reduced biodiversity and impaired key ecosystem functions. Wildlife corridors play an important role in linking detached habitats. The opti...
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The fragmentation of wildlife habitats caused by anthropogenic activities has reduced biodiversity and impaired key ecosystem functions. Wildlife corridors play an important role in linking detached habitats. The optimal design of such corridors considering spatial, ecological and economic factors is addressed in this paper. We present a novel graph-theoretic optimisation approach and a mixed-integer linear programming model to determine an optimal wildlife corridor connecting two given habitat patches. The model maximises the total quality of the corridor and satisfies pre-specified corridor width and length requirements under a resource constraint. Compared to the corridor design models presented in the literature, our model is conceptually simpler, and it is computationally convenient. We applied the model to a real dataset for Eldorado National Forest in California, USA, involving 1,363 irregular land parcels. The model can be extended to design multiple corridors that connect two or more existing habitat patches.
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