Large Distribution Networks Research Articles

Optimal Multi-Objective Placement and Sizing of Distributed Generation in Distribution System: A Comprehensive Review

For over a decade, distributed generations (DGs) have sufficiently convinced the researchers that they are the economic and environment-friendly solution that can be integrated with the centralized generations. The optimal planning of distributed generations requires the appropriate location and sizing and their corresponding control with various power network types to obtain the best of the technical, economical, commercial, and regulatory objectives. Most of these objectives are conflicting in nature and require multi-objective solutions. Therefore, this paper brings a comprehensive literature review and a critical analysis of the state of the art of the optimal multi-objective planning of DG installation in the power network with different objective functions and their constraints. The paper considers the adoption of optimization techniques for distributed generation planning in radial distribution systems from different power system performance viewpoints; it considers the use of different DG types, distribution models, DG variables, and mathematical formulations; and it considers the participation of different countries in the stated DG placement and sizing problem. Moreover, the summary of the literature review and critical analysis of this article helps the researchers and engineers to explore the research gap and to find the future recommendations for the robust optimal planning of the DGs working with various objectives and algorithms. The paper considers the adoption of uncertainties on the load and generation side, the introduction of DGs with energy storage backups, and the testing of DG placement and sizing on large and complex distribution networks.

Real-Time Advanced Energy-Efficient Management of an Active Radial Distribution Network

This article elucidates a new real-time optimization framework for advanced energy-efficient management of active radial distribution networks. The proposed energy management process leverages the benefits of simultaneous deployment of online direct load control (DLC) and conservation voltage reduction (CVR) for decreasing peak energy demand. Initially the proposed problem is designed as a time-coupled stochastic mixed integer nonconvex programming (MINCP) to accommodate long-term offline beneficial aspects in real-time optimization framework, which is later simplified using merger of Queueing theory and Lyapunov optimization. A successive mixed integer linear programming (s-MILP) solution approach is proposed for accurate and fast convergence of the revised MINCP framework. The efficacy of the developed strategy is evaluated after comparing with two-benchmark energy management models (viz. offline and online greedy algorithm) by demonstrating on modified IEEE 69-bus test system. Further scalability of the proposed approach is validated by testing on large distribution networks.

Optimal Preventive Maintenance Planning for Electric Power Distribution Systems Using Failure Rates and Game Theory

Current electric utilities must achieve reliability enhancement of considerable distribution feeders with an economical budget. Thus, optimal preventive maintenance planning is required to balance the benefits and costs of maintenance programs. In this research, the proposed method determines the time-varying failure rate of each feeder to evaluate the likelihood of future interruptions. Meanwhile, the consequences of feeder interruptions are estimated using interruption energy rates, customer-minutes of interruption, and total kVA of service areas. Then, the risk is assessed and later treated as an opportunity for mitigating the customer interruption costs by planned preventive maintenance tasks. Subsequently, cooperative game theory is exploited in the proposed method to locate a decent balance between the benefits of reliability enhancement and the costs required for preventive maintenance programs. The effectiveness of the proposed method is illustrated through case studies of large power distribution networks of 12 service regions, including 3558 medium-voltage distribution feeders. The preventive maintenance plans resulting from the proposed method present the best compromise of benefits and costs compared with the conventional approach that requires a pre-specified maintenance budget.

Data-Driven Predictive Maintenance for Gas Distribution Networks

A generic data-driven approach is presented that employs machine learning to predict the future reliability of components in utility networks. The proposed approach enables utilities to implement a predictive maintenance strategy that optimizes life-cycle costs without compromising safety or creating environmental issues. Any machine learning technique that qualifies as a probabilistic classifier can be employed within the proposed approach. To identify the data-driven model that performs best, a practical metric to assess the performance of the competing models is proposed. This metric is specifically designed to quantify the forecasting performance with respect to maintenance planning. Additionally, a data-driven sensitivity analysis approach is discussed that allows for an assessment of the influence of the different features on the model prediction. Through an application example, it is demonstrated how the proposed approach can be applied to predict future defect rates of pipe sections for maintenance planning in a large gas distribution network.

An Architecture for Distributed Energies Trading in Byzantine-Based Blockchains

With the development of smart cities, not only are all corners of the city connected to each other, but also connected from city to city. They form a large distributed network together, which can facilitate the integration of Distributed Energy Stations (DESs) and corresponding smart aggregators. Nevertheless, because of potential security and privacy protection arising from trustless energies trading, how to make such energies trading go smoothly is a tricky challenge. In this paper, we propose a blockchain-based multiple energies trading (B-MET) system for secure and efficient energies trading by executing a smart contract we design. Because energies trading requires the blockchain in B-MET system to have high throughput and low latency, we design a new byzantine-based consensus mechanism (BCM) based on node’s credit to improve efficiency for the consortium blockchain under the B-MET system. Then, we take combined heat and power (CHP) system as a typical example that provides distributed energies. We quantify their utilities and model the interactions between aggregators and DESs in a smart city by a novel multi-leader multi-follower Stackelberg game. It is analyzed and solved by reaching Nash equilibrium between aggregators, which reflects the competition between aggregators to purchase energies from DESs. In the end, we conduct plenty of numerical simulations to evaluate and verify our proposed model and algorithms, which demonstrate their correctness and efficiency completely.

Self-organized Partition of Distribution Networks with Distributed Generations and Soft Open Points

Islanding operation relying on distributed generations (DGs) is anticipated to be a promising operational measure for resilience enhancement of distribution networks. In this paper, a self-organized network partition framework for controllable DGs (CDGs) is proposed for the distribution network with DGs and soft open points (SOPs) from a game-theoretic perspective. The labeled numbers of the nodes on paths between every two adjacent CDGs are used as the optimization variables in the partition formation, which can avoid a large number of invalid partition schemes. Two objectives including the weighted recovered load and the power loss are considered, which reflect the goals of maximizing the critical load recovery and minimizing the power loss, respectively. The power loss is determined through the AC power flow calculation using Newton-Raphson method considering the operational characteristics of SOPs. The partition problem is modeled as a discrete bi-objective game, which is further transformed into a continuous game model, and the existence of the Pareto-Nash equilibrium is also proved. A decentralized method is developed to solve the proposed bi-objective game relying on the local communications and a global information discovery scheme, which is more practical after the extreme events. Numerical studies on the IEEE 69-bus distribution system and a 135-bus multi-feeder system are performed to validate the effectiveness of the proposed method. The proposed model is applied in different network topologies, and compared with the centralized optimization models. The robustness of the method to communication failures is analyzed, and the scalability of the proposed model is also validated through testing it in a larger distribution network.

A novel multi-objective green vehicle routing and scheduling model with stochastic demand, supply, and variable travel times

We propose an optimization approach to the routing and scheduling problem for a heterogeneous transportation network that considers (i) stochastic values of demand and supply at the nodes, (ii) variable travel times between nodes conditioned by the fatigue of drivers, (iii) maximum allowed continuous driving time, and (iv) soft service time windows per customer at the nodes. The problem minimizes energy consumption and maximizes customer satisfaction. The subsequent stochastic multi-objective mixed integer programming model is solved using a hybrid approach based on chance- and epsilon-constraint methods. Given the NP-hard quality of the model, we introduce a hybrid meta-heuristic method based on genetic algorithm (GA) and simulated annealing (SA). This novel technique, named MOGASA, combines the global and local search capacities of both meta-heuristic algorithms, providing an intuitive solution approach that allows to solve problem instances considering large distribution networks with multiple types of vehicles in reasonable CPU time. We illustrate how MOGASA improves upon the hybrid chance- and epsilon-constraint exact solution method, particularly when dealing with large problem instances that cannot be solved by the latter. A medium instance scenario is used to analyze the reaction of the objective functions and the subsequent Pareto frontiers to modifications in the main structural parameters defining the transportation network. Potential applications of our stochastic framework to different types of logistic structures and retail supply chains are highlighted.

Optimized Voltage-Led Customer Load Active Service Using Genetic Algorithm in Distribution Networks

To mitigate the low frequency problem in a transmission system in an event of a power station failure or during low renewable generation production, UK National Grid (NG) Electricity System Operator has balancing mechanism in place with generators to provide temporary extra power, or with large energy users to reduce load demand or so call fast reserve services. This paper presents an alternative method to aggregately control the existing distribution network primary on load transformer tap changers as a voltage-led customer load active service. The main benefits of the proposed method are (i) to unlock the distribution network load demand flexibility without causing any negative impact on customers, and (ii) to provide the lowest cost of fast reserve service from a distribution network to transmission network. In this paper an optimal control strategy based on genetic algorithm is proposed and developed to achieve an optimized voltage-led customer load active service with the aim of finding the optimal dispatch of on load transformer tap changers by minimizing each transformer tap switching operation as well as network losses. Two practical 102 buses and 222 buses UK distribution networks have been modelled and used to demonstrate and compare the effectiveness of the proposed control methods under different operating conditions. The performances of the proposed method are also compared with both the rule-based and the branch-and-bound methods. The results show that the proposed optimal control strategy based on the genetic algorithm is more effective by achieving more accuracy and a faster solution for a large distribution network than other two methods. These are important findings as the fast reserve service by transmission network requires the accuracy of the load demand reduction delivery within 2 minutes.

A Study On Impact Of White Goods Towards consumers Preference

The market’s level of competition has risen as a result of the advent of the overseas white goods industry. With rising earnings, dual-income families, changing lifestyles, credit availability, greater consumer knowledge, and the introduction of new models by Indian and multinational companies, India’s consumer durable sector is riding the crest of the country’s economic boom. As the Durable market is growing rapidly, an understanding of the consumer behavior regarding the characteristic of consumers in influencing their buying behavior is crucial. Consumer requirements of Air-condition, Washing machine and Refrigerator are at present not limited to its basic function but also on other values like – efficiency, ease of use and comfort. The consumer durables industry is marked by the growth of multinational corporations (MNCs), exchange offers, discounts, and fierce competition. MNCs have a 65 percent market share in the consumer durables industry. The expanding Indian middle class is a major focus for multinational corporations. They compete on the basis of a firm grasp on the local market, well-known brands, and a large distribution network, whereas Indian companies compete on the basis of a firm grasp on the local market, well-known brands, and a large distribution network. Consumer durables penetration, on the other hand, is still low in India. At this juncture, this study has been undertaken for to find out the impact and reality of white goods market in India.

ANALISIS BIAYA INVESTASI PADA PROYEK SISTEM PENGEMBANGAN AIR MINUM DI ZONA 3 KOTA TANGERANG

Drinking water is one of the basic human needs, so important that the 1945 Constitution in Article 33 Paragraph 3 regulates the maximum use of water for the prosperity of the people. Access to drinking water which is the SDG's target, namely 68.87% of Indonesians can be served access to safe drinking water in 2015 especially in 2019, at this time it has not been achieved. The National Medium-Term Development Plan (RPJMN) plans 100% of Indonesians to have access to drinking water by 2019. To achieve this target, additional production capacity and large distribution networks are required. it is necessary to build infrastructure that can support the increasing need for drinking water. Technical planning for the development of a Drinking Water Supply System (SPAM) in zone 3, including the construction of an intake unit, a production unit of 1,000 lps and a distribution unit.

Application of Trust-Region Method in Load Flow Solution of Distribution Network Embedded with DREG

It has been acknowledged that integration of DREG into electrical distribution system will increase the complication of the system load flow analysis. Therefore, development of reliable and efficient solution method to the DSLF problem is crucial so that the steady-state operation of distribution system, including the DREG, can properly be evaluated. In this paper, application of trust-region method in finding load flow solution of modern distribution network containing DREGs is investigated and presented. Trust-region method is a constrained optimization-based technique for solving a set of nonlinear equations. Trust-region method has successfully been applied in load flow analysis of conventional distribution systems (i.e. distribution systems without DGs). Unlike the forward/backward-based method, particular treatments such as branch/bus renumbering/ reordering are not needed in the trust-region method. These special treatments can be difficult to be carried out especially for large distribution networks. Moreover, since optimization method is used in the solution updating process, trust-region computation algorithm is quite robust. This feature is important in distribution system applications, particularly when the system is ill-conditioned. Validity of the proposed trust-region method is confirmed by the results obtained on three representative distribution networks, i.e. 12-bus, 33-bus networks, and 69-bus networks. In addition, the effect of DREGs installation on distribution system steady state performances is also investigated in this paper. Results of the investigation show that the system performances improve when the DREGs installed in the system.

Identifying Critical Thresholds for Resilient Regional Food Flows: A Case Study From the U.S. Upper Midwest

Improving the regional organization of food flow requires an understanding of system constraints. System transformation is necessary if the system is to include regional, independent wholesale food suppliers and to distribute food in an equitable and sustainable manner. Regional suppliers play a pivotal role in overall food system resilience, an emerging issue in wake of the numerous failures in conventional food supply chains exacerbated by COVID-19-related disruptions. Yet alternative supply chains that link local producers with towns and urban centers regionally, represent a small fraction of our nation's food suppliers. They struggle to compete with larger distribution networks that can supply products in-and out-of-season by global procurement. The upper Midwest harbors numerous local and regional food supply chains consisting of farms, processors, trucking companies, wholesalers and other firms that share a commitment to sustainability and local economic development. A constellation of challenges hamper their emergence, however, even as larger scale food supply chains flounder or fail to effectively serve communities. Informed by Donella Meadows's work on leverage points for systemic change, a collaborative, transdisciplinary and systems research effort examined conventional food supply networks and identified key opportunities for shifting food supply chain relationships. System concepts such as stock and flow, leverage points, and critical thresholds helped us to frame and identify challenges and opportunities in the current system. The second and third phase of our collaborative research effort occurred over 4 years (2013–2016) and involved twenty-six people in co-generation of knowledge as a loose-knit team. The team included farmers, supply chain practitioners, students, academic staff and faculty from multiple departments and colleges. Our primary method was to host public workshops with practitioner speakers and participants to identify dominant narratives and key concepts within discourses of different participants in distribution networks. The literature review was iterative, based on challenges, ideas and specific questions discussed at workshops. Our research exposed two meta-narratives shaping the supply chain: diversity and efficiency. In addition to these high-leverage narratives, we identified and examined five key operational thresholds in the Upper Midwest regional food system that could be leveraged to improve food flow in the region. Attention to these areas makes it possible for businesses to operate within environmental limits and develop social structures that can meet scale efficiencies necessary for economic success. We iteratively shared this co-produced knowledge with decision-makers via local food policy councils, local government, and national policy circles with the goal of supplying actionable information. This phased action research project created the environment necessary for a group of food system entrepreneurs to emerge and collaborate, poised to improve system resilience in anticipation of food system disruptions. It forms the basis for on-going research on food flow, regional resilience, and supply chain policy.

Large-Scale Postmarketing Surveillance of Biological Drugs for Immune-Mediated Inflammatory Diseases Through an Italian Distributed Multi-Database Healthcare Network: The VALORE Project.

BackgroundBiological drugs have improved the management of immune-mediated inflammatory diseases (IMIDs) despite being associated with important safety issues such as immunogenicity, infections, and malignancies in real-world settings.ObjectiveThe aim of this study was to explore the potential of a large Italian multi-database distributed network for use in the postmarketing surveillance of biological drugs, including biosimilars, in patients with IMID.MethodsA retrospective cohort study was conducted using 13 Italian regional claims databases during 2010–2019. A tailor-made R-based tool developed for distributed analysis of claims data using a study-specific common data model was customized for this study. We measured the yearly prevalence of biological drug users and the frequency of switches between originator and biosimilars for infliximab, etanercept, and adalimumab separately and stratified them by calendar year and region. We then calculated the cumulative number of users and person-years (PYs) of exposure to individual biological drugs approved for IMIDs. For a number of safety outcomes (e.g., severe acute respiratory syndrome coronavirus 2 [SARS-COV-2] infection), we conducted a sample power calculation to estimate the PYs of exposure required to investigate their association with individual biological drugs approved for IMIDs, considering different strengths of association.ResultsFrom a total underlying population of almost 50 million inhabitants from 13 Italian regions, we identified 143,602 (0.3%) biological drug users, with a cumulative exposure of 507,745 PYs during the entire follow-up. The mean age ± standard deviation of biological drug users was 49.3 ± 16.3, with a female-to-male ratio of 1.2. The age-adjusted yearly prevalence of biological drug users increased threefold from 0.7 per 1000 in 2010 to 2.1 per 1000 in 2019. Overall, we identified 40,996 users of biosimilars of tumor necrosis factor (TNF)-α inhibitors (i.e., etanercept, adalimumab, and infliximab) in the years 2015–2019. Of these, 46% (N = 18,845) switched at any time between originator and biosimilars or vice versa. To investigate a moderate association (incidence rate ratio 2) between biological drugs approved for IMIDs and safety events of interest, such as optic neuritis (lowest background incidence rate 10.4/100,000 PYs) or severe infection (highest background incidence rate 4312/100,000 PYs), a total of 43,311 PYs and 104 PYs of exposure to individual biological drugs, respectively, would be required. As such, using this network, of 15 individual biological drugs approved for IMIDs, the association with those adverse events could be investigated for four (27%) and 14 (93%), respectively.ConclusionThe VALORE project multi-database network has access to data on more than 140,000 biological drug users (and > 0.5 million PYs) from 13 Italian regions during the years 2010–2019, which will be further expanded with the inclusion of data from other regions and more recent calendar years. Overall, the cumulated amount of person-time of exposure to biological drugs approved for IMIDs provides enough statistical power to investigate weak/moderate associations of almost all individual compounds and the most relevant safety outcomes. Moreover, this network may offer the opportunity to investigate the interchangeability of originator and biosimilars of several TNFα inhibitors in different therapeutic areas in real-world settings.Supplementary InformationThe online version contains supplementary material available at 10.1007/s40259-021-00498-3.

Quality zones automatically identified in water distribution networks by applying data clustering methods to conductivity measurements

This paper presents a clustering study showing how conductivity measured every five minutes by 215 probes over four years can be used to determine specific quality zones for a large Water Distribution Network (WDN): 8500 km of pipes, 4.6 M customers. Conductivity time-series are compared using Dynamic Time Warping. Then, probes are ordered using a density-based method, and probe clusters are extracted automatically. The clusters are a sound representation of water quality in the WDN, both in terms of water origin and water residence time. More specifically, zones directly impacted by plants or by external water imports, mixing zones and zones influenced by tanks, can be isolated and analyzed. Globally, 82% of the probes were found to be clustered, consistent with expert knowledge on the WDN operation; 13% were unclassified; 3% were erroneously clustered; and 1% seemed to be reasonably clustered, without any physical understanding yet. Besides providing users with an increased understanding of water quality in WDNs, conductivity-based clusters offer an interesting prior tool for contamination warning systems.

A Self-Organizing Multi-Agent System for Distributed Voltage Regulation

This paper presents a distributed voltage regulation method based on multi-agent system control and network self-organization for a large distribution network. The network autonomously organizes itself into small subnetworks through the epsilon decomposition of the sensitivity matrix, and agents group themselves into these subnetworks with the communication links being autonomously determined. Each subnetwork controls its voltage by locating the closest local distributed generation and optimizing their outputs. This simplifies and reduces the size of the optimization problem and the interaction requirements. This approach also facilitates adaptive grouping of the network by self-reorganizing to maintain a stable state in response to time-varying network requirements and changes. The effectiveness of the proposed approach is validated through simulations on a model of a real heavily-meshed secondary distribution network. Simulation results and comparisons with other methods demonstrate the ability of the subnetworks to autonomously and independently regulate the voltage and to adapt to unpredictable network conditions over time, thereby enabling autonomous and flexible distribution networks.

A Prognostic Energy Optimization Technique with unified power quality conditioner for energy stabilization in grid system

Since the past decade, the power quality system has evolved dynamically due to smart grid technology. In this unique circumstance, energy management and optimal scheduling of different resources are becoming significant and challenging in the Large Scale Distribution Network (LSDN). Due to variable loads connected to the bus system in an uncertain LSDN, the Unified Power Quality Conditioner (UPQC) based compensator is implemented to resolve this difficulty and to compensate the power flow in the bus system. In this work, Prognostic Energy Optimization Technique (PEOT) with UPQC-based self-organizing network is introduced to improve the Optimal Power Flow (OPF); it will also reduce the power loss and enhance power flow in the grid power system. All the bus power flow data are shared in the Internet of Things (IOT) for users to identify the faulty bus location. The performance of the UPQC based 14-bus system is evaluated in MATLAB 2017b software environment.

Compressive sensing based routing and data reconstruction scheme for IoT based WSNs

Data acquisition problem on large distributed wireless sensor networks (WSNs) is considered as a challenge in the growth of Internet of Things (IoT). Recently, the combination of compressive sensing (CS) and routing techniques has attracted much attention of researchers. An open question of this combination is how to integrate these techniques effectively for specific tasks. On the other hand, CS data reconstruction process is considered as one of the CS challenges because it requires to recover N data from only M measurement where M< <N. Through this paper, we propose a new scheme for data gathering in IoT based heterogeneous WSN that includes a new effective Deterministic Clustering using CS technique (DCCS) to handle the data acquisition problem. DCCS reduces the total overhead computational cost needed to self-organize WSN using a simple approach and then uses CS at each sensor node to decrease the overall energy consumption and increase the network lifetime. The proposed scheme includes also an effective CS reconstruction algorithm called Random Selection Matching Pursuit (RSMP) to improve the recovery process at the base station (BS). RSMP adds a random selection process during the forward step to give the opportunity for more columns to be selected as an estimated solution in each iteration. The simulation results show that the proposed scheme succeeds to minimize the overall network power consumption and prolong the network lifetime beside provide better performance in CS data reconstruction.

Multi-Objective User Preference Enabling Method for Service Restoration in Distribution Networks with High Renewable Energy Penetration

Service restoration (SR) in distribution networks by nature is a multi-objective optimization problem. To solve the multi-objective SR and to ensure sufficient available delivery capability for delivering renewable energy, a user preference enabling (UPE) method is proposed in this paper. The proposed method is user-oriented, allowing a user’s preferred solution over a range of each objective function value to be achieved. The proposed UPE method can adopt each user’s specific preference over each objective function, allowing the user to directly place a preference for each objective function without compromising their independence from each other. If the user preference problem has no solution, the proposed method will identify the non-existence of a solution and suggest a remedy scheme to recover a solution. To achieve its computational efficiency, the proposed UPE method contains a discrete optimization stage and a relaxation and correction stage to solve the multi-objective SR problem, which has a large number of binary variables. A user-preferred solution that truly meets the user's needs for each objective is thus fast obtained. The effectiveness and flexibility of the proposed UPE-based method is evaluated on large distribution networks with multiple scenarios.

Efficient and Sustainable Reconfiguration of Distribution Networks via Metaheuristic Optimization

Improving the efficiency and sustainability of distribution networks (DNs) is nowadays a challenging objective both for large networks and microgrids connected to the main grid. In this context, a crucial role is played by the so-called network reconfiguration problem, which aims at determining the optimal DN topology. This process is enabled by properly changing the close/open status of all available branch switches to form an admissible graph connecting network buses. The reconfiguration problem is typically modeled as an NP-hard combinatorial problem with a complex search space due to current and voltage constraints. Even though several metaheuristic algorithms have been used to obtain—without guarantees—the global optimal solution, searching for near-optimal solutions in reasonable time is still a research challenge for the DN reconfiguration problem. Facing this issue, this article proposes a novel effective optimization framework for the reconfiguration problem of modern DNs. The objective of reconfiguration is minimizing the overall power losses while ensuring an enhanced DN voltage profile. A multiple-step resolution procedure is then presented, where the recent Harris hawks optimization (HHO) algorithm constitutes the core part. This optimizer is here intelligently accompanied by appropriate preprocessing (i.e., search space preparation and initial feasible population generation) and postprocessing (i.e., solution refinement) phases aimed at improving the search for near-optimal configurations. The effectiveness of the method is validated through numerical experiments on the IEEE 33-bus, the IEEE 85-bus systems, and an artificial 295-bus system under distributed generation and load variation. Finally, the performance of the proposed HHO-based approach is compared with two related metaheuristic techniques, namely the particle swarm optimization algorithm and the Cuckoo search algorithm. The results show that HHO outperforms the other two optimizers in terms of minimized power losses, enhanced voltage profile, and running time. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article is motivated by the emerging need for effective network reconfiguration approaches in modern power distribution systems, including microgrids. The proposed metaheuristic optimization strategy allows the decision maker (i.e., the distribution system operator) to determine in reasonable time the optimal network topology, minimizing the overall power losses and considering the system operational requirements. The proposed optimization framework is generic and flexible, as it can be applied to different architectures both of large distribution networks (DNs) and microgrids, considering various types of system objectives and technical constraints. The presented strategy can be implemented in any decision support system or engineering software for power grids, providing decision makers with an effective information and communication technology tool for the optimal planning of the energy efficiency and environmental sustainability of DNs.

Methodology for the improvements in synchrophasor based System Integrity Protection Schemes under stressed conditions

Nowadays, the power system is characterized by a conglomeration of ultra and extra-high voltage AC and DC transmission corridors, multi-terminal DC (MTDC), and a new generation having Ultra Mega Power Plants (UMPP). It consists of a large power transmission and distribution network across the country along with old conventional generating units and insertion of Renewable Energy Sources (RES). The System Integrity and Protection Schemes (SIPS) play a crucial role in maintaining a secure and reliable grid and assist in effective grid management during severe power system contingencies. In this article, the operational experiences of three prime important SIPS in India are presented. The demerits in existing SIPS are pointed out and a methodology is proposed to overcome them. The performance and effectiveness of the proposed methodology are evaluated by simulation studies on the 39-bus New England system nested with the HVDC link in MATLAB. Simulation results confirmed the authenticity of a proposed methodology in preserving system integrity. Results are validated using Electrical Transient Analyzer Program (ETAP), which is a very powerful design and analysis tool and has an extremely user-friendly interface.