A daily multiobjective optimization model in smart grids

Large-scale integration of rooftop solar power generation is transforming traditionally passive power distribution systems into active ones. High penetration of such devices creates new dynamics for which the current power distribution systems are inadequate. The changing paradigm of power distribution system requires it to be operated as cyber-physical system. A goal-based holonic multiagent system (HMAS) is presented in this paper to achieve this objective. This paper provides details on design of the HMAS for operation of power distribution systems. Various operating modes and associated goals are discussed. Finally, the role of HMAS is demonstrated for two applications in distribution systems. The first one is associated with control of reactive power at solar photovoltaic installations at individual homes for optimal operation of the system. The second deals with the state estimation of the system leveraging different measurements available from smart meters at homes.

Distribution system is the crucial part of the power system which needs to be monitored for quality since it is the last stage in delivering electricity to consumers. The main aim of suppliers is therefore to maintain excellent quality of supply with minimal support at the network level. This includes dealing with issues around voltage regulation and minimization of real power losses on the network which are reflected in a uniform voltage profile through various load points on the feeders. However, with increase in decentralization, Distributed Generation (DG) becomes the focal point of distribution networks and can be used to achieve the above mentioned objectives. DGs are used in this paper to minimize the losses along with an improvement in the voltage profile. The location of these DG units on the network is addressed as an optimization problem using the loss sensitivity index and voltage deviation index. The size of power injected by DG units is obtained with using an intelligent approach that combines Genetic Algorithm (GA) with neural networks. Different cases of DG in terms of real and reactive power injected are considered resulting in an improvement in voltage profile. The loss sensitivity and voltage deviation indices show the best result for the location of DG needed to minimize the power loss along with improvement in voltage regulation. This approach is tested on a 13-bus distribution network with different types of loads placed throughout the feeder.

The increasing of distributed generators and high demand of customer service quality of recent years keep challenging the design and operation of electric power distribution systems. The future smart distribution grid is envisioned to be a large complex cyber‐physical distribution system (CPDS), where physical grid components are monitored and controlled based on the interactions on the cyber space. As sensors and controllers, smart feeder remote terminal units (FRTUs) transfer necessary data to the control centre for situational awareness, and receive orders from the control centre for dynamic controlling. This study introduces an analytical FRTU deployment approach for the reliability improvement of integrated CPDSs. The FRTU deployment problem is modelled as a mixed‐integer non‐linear programming problem, which aims to minimise the total cost with the reliability index of average service available index as the constraint. Two kinds of distribution feeders (overhead and underground) are considered. The total cost includes the life cycle cost of FRTUs in addition to customer interruption cost. The proposed model can be solved by large‐scale commercial solvers in an efficient manner. Test results of the RBTS‐BUS4 distribution system and a China Southern Power Grid 62‐bus distribution system validate the accuracy and effectiveness of the approach. Comparison with the genetic algorithm also shows its great efficiency.

Coastal cities often face typhoons and urban water logs, which can cause power outages and significant economic losses. Therefore, it is necessary to study the impact of these disasters on urban distribution networks and improve their flexibility. This paper presents a method for predicting power-grid failure rates in typhoons and water logs and suggests a strategy for improving network elasticity after the disaster. It is crucial for the operation and maintenance of power distribution systems during typhoon and water-logging disasters. By mapping the wind speed and water depth at the corresponding positions in the evolution of wind and water logging disasters to the vulnerability curve, the failure probability of the corresponding nodes is obtained, the fault scenario is generated randomly, and the proposed dynamic reconstruction method, which can react in real-time to the damage the distribution system received, has been tested on a modified 33-node and a 118-node distribution network, with 3 and 11 distribution generators loaded, respectively. The results proved that this method can effectively improve the resiliency of the distribution network after a disaster compared with the traditional static reconstruction method, especially in the case of long-lasting wind and flood disasters that have complex and significant impacts on the distribution system, with about 26% load supply for the 33-node system and nearly 95% for the 118-node system.

The need for energy has significantly increased in the world in recent years. Various research works were presented to develop Renewable Energy Sources (RESs) as green energy Distributed Generations (DGs) to satisfy this demand. In addition, alleviating environmental problems caused by utilizing conventional power plants is diminished by these renewable sources. The optimal location and size of the DG-RESs significantly affect the performance of Radial Distribution Systems (RDSs) through the fine bus voltage profile, senior power quality, low power losses, and high efficiency. This paper investigates the use of PV (photovoltaic) and (Wind Turbine) WT systems as a DG source in RDSs. This investigation is presented via the optimal location and size of the PV and WT systems, which are the most used DG sources. This optimization problem aims to maximize system efficiency by minimizing power losses and improving both voltage profile and power quality using White Shark Optimization (WSO). This algorithm emulates the attitude of great white sharks when foraging using their senses of hearing and smell. It confirms the balance between exploration and exploitation to discover optimization that is considered as the main advantage of this approach in attaining the global minimum. To assess the suggested approach, three common RDSs are utilized, namely, IEEE 33, 69, and 85 node systems. The results prove that the applied WSO approach can find the best location and size of the RESs to reduce power loss, ameliorate the voltage profile, and outlast other recent strategies. Adding more units provides a high percentage of reducing losses by at least 93.52% in case of WTs, rather than 52.267% in the case of PVs. Additionally, the annual saving increased to USD 74,371.97, USD 82,127.257, and USD 86,731.16 with PV penetration, while it reached USD 104,872.96, USD 116,136.57, and USD 155,184.893 with WT penetration for the 33, 69, and 85 nodes, respectively. In addition, a considerable enhancement in the voltage profiles with the growth of PV and WT units was confirmed. The ability of the suggested WSO for feasible implementation was validated and inspected by preserving the restrictions and working constraints.

Network reconfiguration is an alternative to reduce power losses and optimize the operation of power distribution systems. In this paper, an encoding scheme for evolutionary algorithms is proposed in order to search efficiently for the Pareto-optimal solutions during the reconfiguration of power distribution systems considering multiobjective optimization. The encoding scheme is based on the edge window decoder (EWD) technique, which was embedded in the Strength Pareto Evolutionary Algorithm 2 (SPEA2) and the Nondominated Sorting Genetic Algorithm II (NSGA-II). The effectiveness of the encoding scheme was proved by solving a test problem for which the true Pareto-optimal solutions are known in advance. In order to prove the practicability of the encoding scheme, a real distribution system was used to find the near Pareto-optimal solutions for different objective functions to optimize.

Traditionally, the electric distribution system operates with uniform energy prices across all system nodes. However, as the adoption of distributed energy resources (DERs) propels a shift from passive to active distribution network (ADN) operation, a distribution-level electricity market has been proposed to manage new complexities efficiently. In addition, distribution locational marginal price (DLMP) has been established in the literature as the primary pricing mechanism. The DLMP inherits the LMP concept in the transmission-level wholesale market but incorporates characteristics of the distribution system, such as high <inline-formula> <tex-math notation="LaTeX">$R/X$</tex-math> </inline-formula> ratios and power losses, system imbalance, and voltage regulation needs. The DLMP provides a solution that can be essential for competitive market operation in future distribution systems. This article first provides an overview of the current distribution-level market architectures and their early implementations. Next, the general clearing model, model relaxations, and DLMP formulation are comprehensively reviewed. The state-of-the-art solution methods for distribution market clearing are summarized and categorized into centralized, distributed, and decentralized methods. Then, DLMP applications for the operation and planning of DERs and distribution system operators (DSOs) are discussed in detail. Finally, visions of future research directions and possible barriers and challenges are presented.

A competitive decentralized framework for Volt-VAr optimization of transmission and distribution systems with high penetration of distributed energy resources

Now day's the demand of the electricity increases as the facilities increase with rise in population. So, utilities have a responsibility to meet the increasing demand without violating the condition of min voltage and current carrying capacity of the conductor. Power quality, voltage regulation, loadability limit, energy efficiency, dependability and availability, and operation under faulted conditions are all key influencing variables in smart distribution system operation. In configuration management, network reconfiguration is a critical operational element. Various configurations have been proposed in the past using various methodologies. In most research, the load is considered to be constant, which is not the case in fact. The distribution system's performance varies depending on the type of load delivered and the loading patterns. In this paper a study is carried out to investigate voltage profile and real power loss with varying load pattern on IEEE 33 bus radial distribution system.

Minimizing power loss and improving voltage stability are crucial aspects of power systems, driven by transmission line contingencies, financial losses for utilities, and potential power system blackouts. Optimal allocation comprising the sizing and operating power factor—of Distributed Generation (DG) units and capacitor banks (CBs) significantly enhances power system efficiency. Efforts by power system operators and researchers focus on addressing issues related to power loss, energy loss, voltage profiles, and voltage stability through the strategic placement of DGs and CBs. Additionally, optimal DG and CB allocation protects the distribution system from unforeseen events and enables operators to run the system in islanding mode when necessary. The integration of DG units and CBs in distribution systems aims to enhance overall system performance. This research paper introduces a Water Cycle Algorithm (WCA) for the optimal placement and sizing of DGs and CBs. The proposed method targets both technical and economic benefits, considering multiple objective functions: minimizing power losses, reducing voltage deviation, lowering total electrical energy costs, and improving the voltage stability index. The WCA emulates the natural water cycle, from streams to rivers and rivers to the sea. Five different operational scenarios are evaluated to test the performance of this methodology. Simulations are conducted on distribution systems: the IEEE 69-bus test system and the Sankhu feeder network, a real system. The results demonstrate the superior performance of the proposed WCA compared to other optimization algorithms. The findings highlight the WCA&amp;apos;s flexibility, efficiency, and significant improvements in economic benefits, establishing it as a promising approach for optimizing the placement of DG and CB in distribution systems.

Advancements in renewable energy sources (RES) have significantly increased power generation and reduced emissions. Optimally integrating RES into distribution systems can minimize power losses, emissions, and enhance voltage profile and stability. Therefore, determining the optimal location and size of RES is crucial for their effective integration. This paper presents a novel approach for identifying the optimal location and size of a solar power plant (SPP) in a distribution system, considering system power losses, voltage profile, voltage stability, and emissions simultaneously. A simple yet effective methodology combining repeated load flow and fuzzy systems is proposed. Repeated load flow is used to calculate the relevant attributes, while fuzzy decision-making is employed to determine the optimal solution. The effectiveness of the proposed method is demonstrated through its application to the IEEE-33 bus system. The results illustrate that integrating a SPP at the optimal location and size can significantly reduce power losses and emissions while improving voltage profile and stability.

The application of Photovoltaic (PV) is one solution to the increasing demand for electrical energy. However, the application of photovoltaic (PV) must be in the right location and capacity so that the power loss you want to reduce is large and the voltage profile is good. Photovoltaic (PV) generates DC voltage which is then required by an inverter to convert it to AC. The inverter is a non-linear load that produces harmonics. Harmonics in an electric power system can be known from Total Harmonic Distortion (THD). The purpose of this study is to determine the optimal location of placement (PV) and its maximum capacity so that the power loss is smaller. The resulting voltage and THD profile conform to the permitted standards. The methods used in determining the optimal location of photovoltaic (PV) are Loss Sensitivity Factor (LSF) and Voltage Sensitivity Index (VSI). ETAP 16 software is used for power and harmonic flow simulation. From this research, the most optimal photovoltaic (PV) placement is on bus 10 (bus 283 T) with a maximum capacity of 3255 kVA. This placement location provides minimal power loss and a good voltage profile taking into account the permitted standard THDv.

Distribution companies (DSCOs) are trying to reduce the high real loss and manage the poor voltage profile, so power loss minimization and voltage profile improvement are the main and important tasks to be faced by electrical engineers in the distribution system (DS). To achieve this goal, distribution system reconfiguration (DSR) is one of the greatest keys and vital analysis to reduce power loss and enhance system voltage profile. In this paper, Modified particle swarm optimization (MPSO) is used for solving the DSR with the objective of minimizing real power loss in the presence of a regular of some constraints. To prove the applicability of the proposed method, MPSO has been applied on operated example the standard IEEE 33-bus system and the results compared with the other popular algorithms. It observed from the numerical results of the test that the real power loss is reduced by 31%, the average system voltage enhanced by 2% and 2.5% improves the minimum voltage.

Integration of distributed generation (DG) in a distribution network is a key for power loss mitigation and improvement in the voltage profile. In this paper, a mixed-integer nonlinear programming (MINLP) based optimization technique has been used to determine the optimal sizes and corresponding locations of DGs to be placed in a distribution network for reduction of power loss and improvement in the voltage profile. The MINLP technique considered in this paper, utilizes the sequential quadratic programming (SQP) through ‘fmincon’ function available in the MATLAB optimization toolbox. The MINLP technique has been applied for solving the problem of optimal DG placement in IEEE 33-Bus and 69-Bus distribution systems. For the purpose of load flow, a well-known direct load flow technique has been used. After the analysis of the results obtained for the two test systems, it can be concluded that MINLP is an effective approach for minimizing the distribution network losses and improving the system voltage profile by optimally placing the DGs of suitable sizes at appropriate locations.

The electrical distribution system (EDS) plays a vital role in the power sector, serving as the final stage in the delivery of electrical energy from the transmission network to end users. It ensures the efficient, reliable, and safe supply of electricity to residential, commercial, and industrial consumers. Over the years, distribution systems have evolved significantly in terms of design, operation, and technology to meet the growing energy demand and improve service quality. This paper surveys existing research, technical developments, and innovations in electrical distribution systems (EDS). It provides a detailed overview of traditional systems mainly radial, ring, and interconnected networks and highlights their characteristics, advantages, and limitations. The study also emphasizes the importance of load management, voltage regulation, and protection schemes that ensure system reliability and minimize power losses. With the integration of renewable energy sources such as solar and wind, as well as the implementation of automation and smart grid technologies, modern distribution systems have become more intelligent and adaptive. The paper explores the role of technologies like Supervisory Control and Data Acquisition (SCADA), smart meters, and Internet of Things (IoT) devices in enhancing monitoring, control, and fault detection. Additionally, challenges such as aging infrastructure, power quality issues, and cybersecurity threats are discussed, along with emerging solutions like energy storage and artificial intelligence–based predictive maintenance. Overall, this survey aims to provide an in-depth understanding of both conventional and modern electrical distribution systems, their operational challenges, and future development trends, contributing to improved system performance and sustainable power distribution in the evolving energy landscape. The findings from this survey consolidate recent advancements and highlight future research opportunities in smart and sustainable distribution systems.