Integration Of Smart Grid Technologies Research Articles

Integration of smart grid technologies: Implementing advanced procurement strategies leveraging AI and machine learning

Integrating smart grid technologies represents a pivotal advancement in modernizing electrical grids and enhancing efficiency, reliability, and sustainability. This publication explores the role of advanced procurement strategies, specifically leveraging artificial intelligence (AI) and machine learning (ML), in streamlining the sourcing and integration of smart grid components. By examining current challenges, potential solutions, and the impact on national security and economic stability, this paper aims to provide a comprehensive framework for the effective implementation of smart grid technologies. The traditional electrical grid, characterized by its centralized generation and unidirectional power flow, faces numerous challenges, such as inefficiency, susceptibility to outages, and inability to integrate renewable energy sources effectively (Brown et al., 2020). The transformation to a smart grid infrastructure, which incorporates digital communication technology and advanced sensors, promises significant improvements. However, the transition is fraught with challenges, particularly in the procurement and integration of the necessary components. One of the primary challenges in smart grid integration is the complexity of supply chains. The procurement of smart grid components involves multiple stakeholders, including manufacturers, suppliers, and regulatory bodies. This complexity often leads to delays, increased costs, and inefficiencies (Smith & Wang, 2019). Additionally, the high initial costs of smart grid technologies pose a barrier to widespread adoption. Traditional procurement methods struggle to balance cost-efficiency with the need for high-quality, durable components, often resulting in budget overruns or compromised quality.

ANALYSIS REDUCTION OF ENERGY LOSSES IN DISTRIBUTION NETWORKS

The article analyzes the reduction of energy losses in distribution networks, which is a serious problem requiring an integrated approach, including technological, regulatory, and behavioral interventions. Some measures that can be developed and implemented to solve this problem are given, such as infrastructure modernization, taking into account investments in the modernization of distribution infrastructure, affecting a significant reduction in energy loss, and the introduction of load management strategies that optimize energy distribution and reduce losses. This includes measures such as load balancing, voltage regulation, and demand management programs to shift peak loads to off-peak hours. The integration of smart grid technologies allows monitoring and management of the distribution network in real-time. This includes the deployment of advanced measurement infrastructure, sensors, and automation systems to more effectively detect and reduce energy losses. Technological losses are associated with the technology of the process of transmission of electricity through networks. Commercial losses are measured by the difference between actual estimated losses and technological losses, taking into account commercial losses, electric energy entering the electric grid, electric energy supplied to consumers, and electricity costs for own needs of substations. Circuit engineering methods for accounting for energy losses in electrical networks are used in various combinations. At the same time, it should be borne in mind that the sequence of reporting operations using the results of specified specific parameters should give an accurate result. To determine the loss of electricity, data on the minimum and maximum load of the network and the amount of electricity consumed over the same period are required. The calculation is given by the method of average load. Information about the minimum and maximum network load is a key element for effective design, management and maintenance of electrical networks. This information allows you to optimize the use of resources and ensure reliable network operation in various conditions. Minimum network load - the minimum load is the minimum amount of energy that is consumed by the network during a certain period of time, for example, during night hours or periods of low activity. Information about the minimum load allows you to optimize network operation, for example, by turning off part of the etquipment or managing energy consumption during off-peak periods. Maximum network load - is the maximum amount of energy consumed by the network at a given time. This may be during periods of peak demand, for example, during a heat wave when a lot of air conditioners are running, or during an increase in production in industrial areas. The maximum load information allows you to determine the required power and capacity of the network equipment to ensure reliable operation under maximum load conditions. Keywords: distribution networks, network load, average load, power, period, power factor, electrical energy, duty cycle, consumers, practical research, voltage.

Smart Grids and Energy Distribution of Technological Disruptions and Innovation of the Petroleum Industry

This paper investigates the transformative impact of smart grids and technological innovations on energy distribution within the petroleum industry, exploring their profound implications for market dynamics and competition. As the global energy landscape undergoes rapid evolution, the integration of smart grid technologies has emerged as a pivotal force, influencing how petroleum companies navigate a changing and increasingly interconnected energy ecosystem.

Coordinated expansion planning of transmission and distribution systems integrated with smart grid technologies

Integration of smart grid technologies in distribution systems, particularly behind-the-meter initiatives, has a direct impact on transmission network planning. This paper develops a coordinated expansion planning of transmission and active distribution systems via a stochastic multistage mathematical programming model. In the transmission level, in addition to lines, sitting and sizing of utility-scale battery energy storage systems and wind power plants under renewable portfolio standard policy are planned. Switchable feeders and distributed generations are decision variables in the distribution level while the impact of demand response programs as a sort of behind-the-meter technologies is accommodated as well. Expansion of electric vehicle taxi charging stations is included as a feasible option in both transmission and distribution levels. In order to deal with short-term uncertainty of load demand, renewable energy sources output power, and the charging pattern of electric vehicle taxis in each station, a chronological time-period clustering algorithm along with Monte Carlo simulation is utilized. The proposed model is tackled by means of Benders Dual Decomposition (BDD) method. The IEEE RTS test system (as the transmission system) along with four IEEE 33-node test feeders (as distribution test systems) are examined to validate effectiveness of the proposed model.

Power Situation in Oman and Prospects of Integrating Smart Grid Technologies

The sultanate of Oman maintained a stable development growth in the last 50 years. Consequently, the electricity sector in Oman continued to keep pace with the resulting development, given the size of the electricity network and the new technologies used in the different levels of the power system. This paper presents the current power situation in Oman, considering the prospects of the penetration of smart grid technologies with the national grid. The paper gives an extensive review of Oman power system, with regards to the possible locations of wind and solar energy potentials. The roles of Information and Communication Technology (ICT), and the Data Management Scheme (DMS) smart technologies were also presented with respect to the Oman national grid. Furthermore, the topologies of energy sales and smart metering were considered as some of the salient benefits of the integration of smart grid technologies in the Sultanate power grid.

Advanced Laboratory Testing Methods Using Real-Time Simulation and Hardware-in-the-Loop Techniques: A Survey of Smart Grid International Research Facility Network Activities

The integration of smart grid technologies in interconnected power system networks presents multiple challenges for the power industry and the scientific community. To address these challenges, researchers are creating new methods for the validation of: control, interoperability, reliability of Internet of Things systems, distributed energy resources, modern power equipment for applications covering power system stability, operation, control, and cybersecurity. Novel methods for laboratory testing of electrical power systems incorporate novel simulation techniques spanning real-time simulation, Power Hardware-in-the-Loop, Controller Hardware-in-the-Loop, Power System-in-the-Loop, and co-simulation technologies. These methods directly support the acceleration of electrical systems and power electronics component research by validating technological solutions in high-fidelity environments. In this paper, members of the Survey of Smart Grid International Research Facility Network task on Advanced Laboratory Testing Methods present a review of methods, test procedures, studies, and experiences employing advanced laboratory techniques for validation of range of research and development prototypes and novel power system solutions.

Economic valuation of smart grid investments on electricity markets

Abstract Many countries are establishing regulatory guidelines to promote the integration of smart grid technologies with the aim of improving the efficiency of the power system. This paper analyzes how the investment costs of the different smart grid applications installed by network operators can be recovered in the short-term when a locational marginal pricing scheme is applied. This issue has been previously addressed in literature with simple economic models where the multilevel structure of the smart grid is not considered. As a key contribution, this paper presents a new social welfare optimization model considering implementation strategies with different expenditure levels in smart grid technologies where specific applications as renewable distributed generation, network automation, demand–response and reactive power management are explicitly considered. The proposed model has been implemented using a 3-node illustrative example and a 149000-node network under real-world conditions. Results show how a progressive implementation of smart grid applications can produce increased system efficiency and social welfare, in particular when demand–response is enabled. Results also show how the costs of demand–response applications applied in weak power systems are suitable to be recovered in the short-term with marginal revenues of the network provider.

Integration of smart grid technologies in stochastic multi-objective unit commitment: An economic emission analysis

This paper proposes a stochastic multi-objective unit commitment (SMOUC) problem incorporating smart grid technologies (SGTs), namely, plug-in electric vehicles (PEVs), demand response programs (DRPs), compressed air energy storage (CAES) units, and renewable distributed generations (DGs). An economic emission analysis of the proposed SMOUC problem with the SGTs is carried out to minimize the total expected operation cost and emission using a new mixed-integer linear programming (MILP) method. A two-stage stochastic programming method is used for dealing with the uncertain nature of power generation from the renewable DGs. Lexicographic optimization in combination with hybrid augmented-weighted ε-constraint method are employed to obtain Pareto optimal solutions of the SMOUC problem, and a fuzzy decision making is applied to select the most preferred non-dominated solution. Besides, mathematical modeling of responsive loads can help the independent system operator (ISO) to use a conservative and reliable model to have lower error in load curve characteristic estimation, such as variation in peak load. In this regard, this paper also contributes to the existing body of knowledge by developing linear and nonlinear economic models of price responsive loads for time-based DRPs (TBDRPs), as well as voluntary and mandatory incentive-based DRPs (IBDRPs) based on the customer’s behavior (CB) concept and price ratio (PR) parameter. Also, new mathematical indices are proposed to choose the most conservative and reliable economic model of price responsive loads. Moreover, different widely used DRPs are analyzed and prioritized using the strategy success index (SSI) from the ISO viewpoint to determine the most effective DRP which has more coordination with the SGTs. The proposed MILP-based SMOUC problem with integrated SGTs, is applied to IEEE 10-unit test system and is implemented in General Algebraic Modeling System (GAMS) environment. Simulation analyses demonstrate the effectiveness of integrating SGTs into the proposed SMOUC problem from the economic, environmental, and technical points of view.

How Smart Grid Contributes to Energy Sustainability

With the increasingly serious energy shortage and global warming, sustainable development has become an urgent requirement all over the world. The integration of smart grid technologies, sustainable energy resources and low-carbon emissions in power system is an important route to sustainable development. However, the difficulties in dealing with intermittent power and the low utilization efficiency of power system appeared to be obstacles. This paper gives an overview of the role smart grid playing in energy sustainability. Firstly, smart grid techniques improve the amount of intermittent renewable generation in power system, increasing the capacity of grid-connected clean energy such as solar energy, wind energy and photovoltaic system. Secondly, smart grid promotes energy saving in power system. The main advantage of smart grid is that it can improve the utilization efficiency of power system and the power consuming efficiency. Lastly, this paper discusses the interrelationship of energy, environment and climate sustainable development and draws the conclusion that smart grid can make a significant and comprehensive contribution to energy and environment sustainability, and also helps to control climate change.