An adaptive optimization model for power conservation in the smart grid

Model predictive control (MPC) is a method to formulate the optimal scheduling problem for grid flexibilities in a mathematical manner. The resulting time-constrained optimization problem can be re-solved in each optimization time step using classical optimization methods such as Second Order Cone Programming (SOCP) or Interior Point Methods (IPOPT). When applying MPC in a rolling horizon scheme, the impact of uncertainty in forecasts on the optimal schedule is reduced. While MPC methods promise accurate results for time-constrained grid optimization they are inherently limited by the calculation time needed for large and complex power system models. Learning the optimal control behaviour using function approximation offers the possibility to determine near-optimal control actions with short calculation time. A Neural Predictive Control (NPC) scheme is proposed to learn optimal control policies for linear and non-linear power systems through imitation. It is demonstrated that this procedure can find near-optimal solutions, while reducing the calculation time by an order of magnitude. The learned controllers are validated using a benchmark smart grid.

peer reviewed

Guest Editorial

Economic value of response from sire selection in dairy cattle—A linear programming model

This paper describes three models developed through the Western Electricity Coordinating Council (WECC) SVC Task Force, to represent static Var systems (SVS) for power-flow and time-domain stability simulations. The goal was to develop a set of model structures that are generic, and can be easily parameterized to represent a variety of SVS systems. The term generic is used to imply a model structure that is not specific to a given vendor or equipment, and that is non-proprietary and public. These models have been implemented by several software vendors, and may soon be adopted by others. These models offer: 1) a suitable non-proprietary, not vendor specific set of models that can be used to evaluate SVS solution options for planning studies, and 2) the means to move away from the proliferation of user-written models that are becoming hard to manage in large interconnected power system models such as the WECC. The dissemination of the models and modeling documentation helps provide guidance to power system planners and operators about the latest SVS technologies and their application.

5G-Wireless Sensor Networks for Smart Grid-Accelerating technology’s progress and innovation in the Kingdom of Saudi Arabia

WSNs (Wireless Sensor Networks) have been considered one of the very promising technologies for the implementation of smart grid. And security of WSNs becomes a central concern. The security solutions for generic wireless sensor networks cannot be directly used in smart grid WSNs. In this paper, the applications of sensor networks in electric power systems are discussed and analyzed first. Then, the characteristics of smart grid WSNs are summarized. Threats and security requirements special for wireless sensor networks used in smart grid systems are presented. Based on these works, reference security architecture was proposed to guide the development and the design of the security solutions of wireless sensor networks in smart grid systems, considering the information security requirements of electric power systems. Moreover, open security issues needed solve to protect WSNs applied in smart grid, and research challenges are introduced.

Digital Twin technology is rapidly emerging as a transformative force in various industries, playing a pivotal role in the advancement of Industry 4.0. As industries evolve, the implementation of Digital Twin is anticipated to drive significant technological progress across sectors such as manufacturing, smart cities, healthcare, and transportation. This paper presents a layered Digital Twin framework that synthesizes recent advances in sensing, data analytics, and machine learning to enable real-time monitoring, analysis, and predictive capabilities for modern power systems. By leveraging these advanced sensors, the Digital Twin framework enables more accurate system modeling, enhanced situational awareness, and improved decision-making processes. This approach aims to significantly transform traditional smart grid operations by offering a more responsive, resilient, and efficient energy management system, ultimately contributing to the optimization and modernization of existing power infrastructure. Furthermore, by highlighting key smart grid applications, the paper demonstrates the effectiveness of Digital Twin and sensor technology in optimizing power distribution, reducing downtime, and addressing prevalent energy challenges, thereby paving the way for resilient and intelligent power systems.

The emerging clean-energy smart grid environment in the electric power sector has necessitated that related educational programs evolve to meet the needs of students, faculty, and employers alike. In order to prepare the next generation of power engineering professionals to meet the challenges ahead in the electric power sector, a new curriculum must be developed that includes core power engineering principals coupled with emerging aspects of smart grid technologies and clean energy integration. Such curriculum also needs to consider not only the end-use side of the power system within the smart grid definition, such as smart metering, communications and demand response aspects, but also other key enabling technologies throughout the whole transmission and distribution system and the entire energy supply chain. These include areas such as energy storage technologies, advanced power electronics at the transmission and distribution levels, networked control systems, automation, renewable and alternative energy systems integration, system optimization, real-time control, and other related topics. In addition, the evolution of power programs and curriculum in this emerging area must take into account significant input from industry constituents engaged in the manufacturing, implementation, operation, and maintenance of the new smart grid technologies and systems. By working collaboratively with industry to meet future employer needs, programs with newly developed course offerings will be able to better prepare students and existing professionals alike for the rapidly growing clean-energy, smart grid environment. This paper will provide an overview of a potential model for program structures and course developments in this critical area, including examples of initiatives already being developed and deployed.

Wireless Sensor Networks (WSNs) have been widely recognized as a promising solution for enhancing various aspects of electric power grid and realizing the vision of smart grid. However, energy crisis and challenging wireless environment in smart grid create a number of challenges for WSNs, as a result of which energy efficiency become critically important. On the other hand, cognitive radio (CR) technology is expected to play a vital role in smart grid networks. Cognitive Sensor Networks (CSNs) can effectively address some unique challenges of WSNs in smart grid. In this paper, we aim to design an energy efficient Medium Access Control (MAC) protocol for CSNs. In this regard, we propose ECR-MAC, which is an energy-efficient receiver-based MAC protocol for CSNs. ECR-MAC uses a energy-efficiency based auction mechanism and preamble sampling techniques for providing high energy efficiency and reliability. In addition, ECR-MAC explicitly accounts for the peculiarities of a CR environment. Analytical and simulation results demonstrate the effectiveness of ECR-MAC as a viable solution for CSNs.

Guest Editorial: Situational awareness of integrated energy systems

Linear programming is an important branch of operations research. The model is mature in theory and widely used in real life. However, various complex realistic scenarios involve fuzzy information. In this paper, we consider a fuzzy linear programming (FLP) model in which all parameters are trapezoidal interval type-2 fuzzy numbers (IT2FNs) and propose a solution method based on the nearest interval approximation and the best-worst cases (BWC) method. We prove the nearest interval approximation interval of trapezoidal IT2FNs, then the trapezoidal IT2FNs in the model are transformed into interval numbers which both upper and lower limits are interval numbers. With the help of best-worst cases (BWC) method, the sub-models of the transformed interval linear programming model are proposed, and four sub-solutions with different specific meanings can be obtained by solving them respectively. Finally, an application example is presented to show the rationality and practical significance of the method.

Linear goal programming in estimation of classification probability

A time domain method for accurate modeling of external power systems in electromagnetic transients programs (EMTPs) is presented. The time domain response of the external system to a multisine excitation signal is utilized to identify a discrete-time model for the external system. The identified model parameters are determined by least-squares estimation. The details of building both single-phase and multiphase equivalents are given. The validity of the equivalents is verified by comparing the electrical transients generated due to the energization of an open-ended line using the full and the equivalent external system. The method is being extended to multiport equivalents.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

This paper investigates the optimal cash crop combination at a small-scale cash crop farm in Vhembe District, Limpopo Province, South Africa in which green maize, cabbages, tomatoes, spinach, mustard, butternut and sweet potatoes are grown. To get optimum farm outputs, decisions on crop combination and operational activities in crop production are crucial. Proper farm planning and resource allocation play a significant role in optimising farm revenues. It was observed that the farmer used traditional methods of allocating resources, which lead to a less profitable crop mix. In view of this, in this study, linear programming model was formulated using data collected from a farm concerning the past crop combinations and allocation of resources in crop planning and production to determine the best crop combination that maximizes net income given limited resources such as land, labour, capital, and others. The simplex method of linear programming works by first locating a feasible solution and then relocating to any vertex of the feasible set that improves the cost function. Eventually, a point is reached beyond which no further movement improves the cost function. The results of the developed linear programming model were compared to past farming practices based on experience, leading to the conclusion that crops and limited resources were not optimally allocated. The results clearly demonstrate the optimal crop combination and allocation of scarce resources that the farm could have considered to yield maximum returns. It is observed that the proposed linear programming model is appropriate for finding the optimal land allocation criteria for the cash crops in the study area. The optimal crop mix from the linear programming two phase simplex method show that the farmer should grow the following crop mix, 1.16 ha of green mealies, 2.64 ha of cabbages, 0.8 ha of tomatoes, 1.2 ha of mustard, 0.4 ha spinach, 0.4 ha of butternut and 0.4 ha of sweet potatoes with the gross income of R 740 800. The linear programming model resulted in a 37.8 percent increase in profit margin. Based on the results obtained from this study, it is recommended that the small-scale cash crop farmer should invest more in producing crops that give maximum profit. MATLAB software was used to determine the optimal values of the decision variables. Key words: Cash crops allocation, crop combination, net income, linear programming (LP), resource allocation.