Energy Management
Sh. Shadi; J. Salehi; A. Safari
Abstract
Energy management (EM) in smart distribution networks (SDN) is to schedule the power transaction between the SDN and the existing distributed energy resources (DERs) e.g., distributed generations, especially renewable resources and electrical vehicles, from an eco-technical viewpoint. Due to the dual ...
Read More
Energy management (EM) in smart distribution networks (SDN) is to schedule the power transaction between the SDN and the existing distributed energy resources (DERs) e.g., distributed generations, especially renewable resources and electrical vehicles, from an eco-technical viewpoint. Due to the dual role of electric vehicles (EVs) acting as a power source and load, they presented both challenges and opportunities in EM. The complexity of EM increases as DERs become more prevalent in SDN. Moreover, the uncertainties of renewable resources, price, and load besides the uncertainties related to the place, amount, and time of EV’s charging makes EM a more intricate field. This supports the necessity of extensive tools and approaches to manage EM in SDNs. In this respect, this paper proposes an optimum scenario-based stochastic energy management scheme for intelligent distribution networks. The proposed approach is modeled as a MINLP problem and solved in GAMS software under the DICOPT solver. The test is conducted on a 33-bus SDN with and without factoring in uncertainties.
A. Namvar; J. Salehi
Abstract
One of the crucial challenges within the optimal operation of smart cities is coordinated management of multiple energy carriers in the residential buildings owing to disparate and often conflicting objectives. In response to this challenge, this paper proposes a novel conceptual cost-emission-based ...
Read More
One of the crucial challenges within the optimal operation of smart cities is coordinated management of multiple energy carriers in the residential buildings owing to disparate and often conflicting objectives. In response to this challenge, this paper proposes a novel conceptual cost-emission-based scheme for optimal energy-gas use in a smart home in the context of residential energy hubs considering a meaningful trade-off between cost saving and environmental protection. Various energy conversion resources containing energy and heat storage systems, rooftop photovoltaic modules, and also combined heat and power units along with responsible electrical and thermal loads are taken into account in the proposed model. Furthermore, an efficient stochastic scenario-based method is executed to tackle the intense uncertainty associated with photovoltaic production. The proposed model reduces domestic energy consumption and utility costs by incorporating a weighted summation mixed objective function under various system constraints and user preferences, while at t the same time optimal task scheduling and comfort for the resident that it can guarantee a good lifestyle. The presented scheme is carried out on a realistic case study equipped with energy hubs and as expected, introduces its applicability and effectiveness in the optimal energy management of the proposed residential energy hub problem. The simulation results confirm that energy procurement costs can be saved by up to 46.16% and emission costs by 34.07% while maintaining the desired level of comfort for the head of the household.
A.M. Alee; S. Golshannavaz; T. Ghanizadehbolandi; V. Talavat
Abstract
This paper presents a novel method to improve the efficiency of active distribution networks (ADNs) by optimal placement of distributed energy resources (DERs) and utilizing the unused capacity of inverter-interfaced photovoltaic (PV) units for reactive power compensation. After investigating the mathematical ...
Read More
This paper presents a novel method to improve the efficiency of active distribution networks (ADNs) by optimal placement of distributed energy resources (DERs) and utilizing the unused capacity of inverter-interfaced photovoltaic (PV) units for reactive power compensation. After investigating the mathematical model of PV systems, wind turbines, other non-renewable distributed generations, energy storage systems, and responsive loads, a genetic algorithm (GA)-based approach is used to find the optimal placement and allocation of all units. The modeling also takes into account the uncertainty of PV units and wind turbines to represent real-world operational conditions more accurately. Additionally, although the IEEE 33-bus system is used to formulate the presented method, one can easily extend it to any other network with an arbitrary number of buses. The effectiveness of the proposed method is verified by designing three different scenarios. The simulation results obtained based on MATLAB clearly show the capability of the proposed method to improve the voltage profile and the cost of losses in ADN. This is done by properly utilizing the excess capacity of inverter-interfaced PV units as a static compensator (STATCOM), even in the absence of sunlight. The findings indicate that the inclusion of DERs and PV-STATCOM results to a notable enhancement of approximately 68.46% in power losses reduction and around 65% in the voltage deviation minimization.
Energy Management
S.R. Kumisbekovna; G. Kakimzhan; N. Darimbayeva; A. Besterekova; Z. Toygozhinova; E. Darkenbaeva; M. Sakitzhanov
Abstract
Traditional energy management focuses on ensuring a reliable and sustainable energy supply through meticulous planning, coordination, and optimization of resources. However, integrating renewable energy sources like solar, wind, and hydropower introduces a new layer of complexity. These sources, while ...
Read More
Traditional energy management focuses on ensuring a reliable and sustainable energy supply through meticulous planning, coordination, and optimization of resources. However, integrating renewable energy sources like solar, wind, and hydropower introduces a new layer of complexity. These sources, while environmentally friendly, are inherently intermittent and variable in their production, posing unique challenges for energy management. Effective energy management in the presence of renewable energy requires strategies to balance supply and demand, optimize energy use, and ensure grid stability. This study introduces a new model designed to significantly improve the accuracy of estimating both energy production and demand. This enhanced level of precision plays a decisive role in the decision-making process for energy management. This innovative model employs a fuzzy neural network trained on historical energy production data, integrating weather information through fuzzy functions to improve precision in estimating energy production for future intervals. The objective functions prioritize renewable energy use to minimize overall system costs. The simulations evaluated the total system cost under various conditions. The results showed that more accurate estimation and maximized utilization of renewable energy sources led to a significant reduction in the cost per kilowatt-hour. In essence, this study offers a promising approach to managing energy systems that heavily rely on renewable sources. By improving estimation accuracy and prioritizing renewable energy use, the model paves the way for a more reliable, sustainable, and cost-effective energy future.
Energy Management
G.R Aghajani; I. Heydari
Abstract
Microgrid and smart electrical grids are among the new concepts in power systems that support new technologies within themselves. Electric cars are some advanced technologies that their optimized use can increase grid efficiency. The modern electric cars sometimes, through the necessary infrastructure ...
Read More
Microgrid and smart electrical grids are among the new concepts in power systems that support new technologies within themselves. Electric cars are some advanced technologies that their optimized use can increase grid efficiency. The modern electric cars sometimes, through the necessary infrastructure and proper management, can serve as an energy source to supply grid loads. This study was conducted to investigate the energy management for production and storage resources. For this purpose, we considered the market price of energy, the prices quoted by distributed generation sources, and electric vehicles in the grid and responsive loads. The load response programs used include the time of use and direct load control. The problem has a linear mixed-integer planning structure that was simulated using the GAMS software. The results show that with this planning, the proposed load response programs have a positive impact on cost reduction.
Energy Management
M. Ahangari Hassas; K. Pourhossein
Abstract
Hybrid renewable energy systems (HRES) have been introduced to overcome intermittent nature of single-source renewable energy generation. In order to utilize HRES optimally, two issues must be considered: optimal sizing and optimal operation. The first issue has been considered vastly in several articles ...
Read More
Hybrid renewable energy systems (HRES) have been introduced to overcome intermittent nature of single-source renewable energy generation. In order to utilize HRES optimally, two issues must be considered: optimal sizing and optimal operation. The first issue has been considered vastly in several articles but the second one needs more attention and work. The performance of hybrid renewable energy systems highly depends on how efficient the control of energy production is. In this paper, paradigms and common methods available for control and management of energy in HRES are reviewed and compared with each other. At the end, a number of challenges and future research in relation to HRES are addressed.
