• Energy Research

Wind energy has become one of the most cost-effective renewable sources nowadays. However, the stochastic nature associated with wind-energy production represents a great challenge for power-system operations. Therefore, probabilistic techniques are necessary to evaluate the performance of power systems with substantial amounts of wind generation. This paper presents a probabilistic based bi-level optimization approach for evaluating the impact of wind farm location and control strategy on the penetration level of wind farms and electricity market prices. The bi-level optimization model is formulated as mathematical program with equilibrium constraints (MPEC) and solved by means of the NLPEC solver in the General Algebraic Modeling System (GAMS) environment. Several cases studies are presented in this paper to determine to the optimal wind generation penetration and market prices with different locations and control strategies for wind farms. Moreover, some scenarios are discussed in regards to the practical allocation of wind farms.

Smart grids have been emerging nowadays as an initiative to operate modern distribution systems in a more economic and efficient way. Energy storage systems (ESSs) are one of the promising technologies that can achieve the goals of smart grids via facilitating the connection of renewable sources, improving system reliability, and controlling the net demand through peak load shaving, etc. In this paper, a comprehensive planning framework is introduced for ascertaining the most cost effective siting and sizing of ESSs that maximize their benefits in distribution networks. A probabilistic approach is further adopted that includes the consideration of the stochastic nature of system components. Such approach allows determining the optimal operation of ESS at each load state. Moreover, contingency planning decisions, in the form of load points to be shed during contingencies, are identified through the approach proposed.

The emerging interest in deployment of plug-in electric vehicles (PEVs) in distribution networks represents a great challenge to both system planners and owners of PEV-parking lots. The owners of PEV-parking lots might be interested in maximizing their profit via installing charging units to supply the PEV demand. However, with stringent rules of network upgrades, installing these charging units would be very challenging. Network constraints could be relaxed via controlling the net demand through integrating distributed generation (DG) and/or storage units. This paper presents an optimization model for determining the optimal mix of solar-based DG and storage units, as well as the optimal charging prices for PEVs. The main objective is to maximize the benefit of the PEV-parking lot's owner without violating system constraints. Two cases are considered in this paper: uncoordinated and coordinated PEV demand. A novel mathematical model is further developed whereby the behavior of vehicles' drivers, in response to different charging prices, is considered in generating the energy consumption of PEVs.

The integration of renewable distributed generation (RDG) into distribution networks is promising and increasing nowadays. However, high penetration levels of distributed generation (DG) are often limited as they may have an adverse effect on the operation of distribution networks. One of the operation challenges is the interaction between DG and voltage-control equipment, e. g., an under-load tap changer (ULTC), which is basically designed to compensate for voltage changes caused by slow load variations. The integration of variable DGs leads to rapid voltage fluctuations, which can negatively affect the tap operation of ULTC. This paper investigates the impact of high penetration levels of RDG on the tap operation of ULTC in distribution networks through simulations. Various mitigation techniques that can alleviate this impact are also examined. Among these techniques, constant power-factor mode is regarded as the best trade-off between the simplicity and effectiveness of minimizing the number of tap operations. Simulations are performed on a Canadian benchmark rural distribution feeder using OpenDSS software.

AbstractRecent deployment of distributed generation has led to a revolution in distribution systems and the emergence of “smart grid” concepts. The smart grid mainly intends to facilitate integration of renewable energy sources and to achieve higher system reliability and efficiency. Different distributed generation types found in modern distribution systems can be classified into two main categories: intermittent- and dispatchable-based distributed generation; the latter has a unique capability of enabling successful islanding operation, thus improving system reliability. This article proposes a methodology for allocating dispatchable distributed generation units in distribution systems to economically improve system reliability. Distributed generation installation and operation costs are to be optimized against the reliability value, expressed as customers’ “willingness to pay” (WTP) to avoid power interruptions. Therefore, the main goal of this research work is to determine the optimal combination betw...

Photovoltaic (PV) systems are prone to partial shading (PS) due to the environmental factors that they function in such as vegetation, nearby structures, and clouds. All types of PS scenarios can lead to power loss and hot spots in the PV system due to module mismatch and heating of shaded cells. To mitigate the power loss that occurs due to PS, it is imperative to detect PS and its characteristics, such as the number of shaded modules and the associated shading factor (SF), in a reliable manner. This paper proposes a three-step framework to detect and locate PS, the number of shaded modules, and the SF in the PV system using a random forest (RF)-based approach. The proposed approach utilizes independent string current and voltage measurements to distinguish different PS scenarios. This approach allows for a scalable data acquisition through an uncoupled modeling scheme. PS, the number of shaded modules and the SF are deduced with accuracies of 99.5%, 92.3%, 90.2%, respectively. Further, the proposed approach is validated through two testing tiers, and its ability to detect multiple PS scenarios in a PV system has been highlighted. The results observed through different PS scenarios confirm the high reliability and demonstrate the effectiveness and scalability of the proposed RF-based approach.