Network Constraints Research Articles

Analyzing impact of network constraints on feasible operation region of the radial distribution networks

Recently, there has been lot of research on finding the feasible operating region (FOR) of the active distribution network (ADN) to provide ancillary services to the transmission grid. The current focus of research is on finding methods that can accurately determine the shape of the FOR and have fast computation time. However, there is little focus on analyzing how network constraints impact the shape of the FOR. This paper attempts to address this issue and provides a mathematical derivation of how network constraints, such as cable loading and voltage limits, would influence FOR. The approach is based on a linear distflow mathematical analysis. The relations derived in the analysis are verified with numerical simulation on radial low-voltage (LV) and medium-voltage (MV) networks. The results show that cable loading constraints cut the FOR in a circular way and voltage constraints cut the FOR with a straight line of slope −ϵ, which is the ratio Rn/Xn of the path from root node to the node under analysis of the radial distribution network.

Chance constrained optimal power-water flow: An iterative algorithm assisted by deep neural networks

The optimal power-water flow (OPWF) is a fundamental tool for operating integrated power and water networks. To address uncertainties in renewable energy and load demands, researchers have used chance-constrained models to investigate OPWF under uncertainty. However, existing work has limitations, such as oversimplified network representations, intractable problem formulations, and high computational burdens. This paper presents a chance-constrained OPWF formulation that incorporates detailed network constraints and stochastic uncertainties from wind speeds and electricity/water demands. By leveraging the concept of quantiles of stochastic variables and convexification techniques, the model is recast into a deterministic mixed-integer second-order cone programming (MISOCP) problem with uncertain margins. To efficiently solve this problem, we propose a novel iterative algorithm assisted by deep neural networks (DNNs). The algorithm integrates two DNNs: one designed to solve power and water flow equations, accelerating the probabilistic evaluation of uncertain margins; and the other to predict pipeline flows and refine variable ranges, facilitating a warm-start approach to the MISOCP model. Case studies conducted on the IEEE 123-node feeder coupled with a 67-node water distribution network validate the two DNNs and the overall iterative algorithm. The findings demonstrate that the proposed DNN-assisted approach effectively balances system security and economy. Compared to existing algorithms, the relative error of the optimization solution is less than 0.03%, with a speedup ratio exceeding 200.

A novel surrogate polytope method for day-ahead virtual power plant scheduling with joint probabilistic constraints

Virtual Power Plant (VPP) is an emerging concept that can effectively manage a large number of distributed energy resources (DERs). However, the inherent uncertainty of load and renewable generation poses a challenge to reliable VPP power scheduling. This manuscript proposes a novel method for day-ahead VPP scheduling with a joint probabilistic guarantee on its power availability without violating the DER constraints and network constraints. A surrogate polytope is first used to find the inner approximation of the VPP power, implicitly including the low-level DER power, DER constraints, and network constraints. Then, a multivariate Gaussian distribution is used to fit the random parameters of the surrogate polytope, after which the iterative supporting hyperplane algorithm is used to solve the VPP scheduling problem. Extensive case studies based on real-world renewable generation scenarios demonstrate the superior performance of the proposed method in out-of-sample cost and reliability, with a manageable computing complexity.

DFCAFNet: Dual-feature co-attentive fusion network for diabetic retinopathy grading

Diabetic retinopathy (DR) grading is a complicated task characterized by inter-class variations among several categories, the subtle detection of tiny lesions, and uneven data distribution. To achieve precise DR grading, extracting distinct features that can effectively capture minor visual variations in lesions is necessary. The constraints of conventional convolutional neural networks (CNNs) are evident in their difficulty in detecting tiny lesions, which is further enhanced by their biased focus due to an uneven distribution of data. Furthermore, comparable color and texture attributes among different classes add to the complexity of achieving accurate grading results. This paper presents a new multi-scale attention fusion network (MS-AFNet) incorporating multi-scale contextual information. By successfully combining local and global features, the network improves the diagnostic accuracy of the model in handling inter-class variances. A concurrent attention module (CAM) with multiple attention blocks enhances the ability to distinguish information by capturing complex features within the lesions through numerous attention mechanisms. This novel approach greatly improves the model’s capacity to distinguish nuanced features in minor lesions, resulting in more precise diagnostic outcomes. A dual-feature co-attentive fusion network (DFCAFNet) is proposed by combining CAM and MS-AFNet, which capture spatial patterns and highlight inter-channel interactions to detect complex patterns and enhance accuracy. An extensive assessment of two datasets on DR demonstrates the exceptional accuracy and computational efficiency of DFCAFNet.

Enhancing DC distribution network efficiency through optimal power coordination in lithium-ion batteries: A sparse nonlinear optimization approach

This work deals with the problem regarding the optimal operation of lithium-ion batteries to improve the economic, technical, and environmental indices of standalone and grid-connected direct current (DC) distribution networks.To this effect, a general nonlinear programming model has been developed, which considers three objective functions: (i) the daily energy purchasing costs at the terminals of the substation, considering the maintenance costs of renewable generation (photovoltaic) and energy storage technologies (batteries); (ii) the daily energy losses associated with energy transport; and (iii) the CO2 emissions per day of operation, associated with conventional generators. This is done by integrating all the operations and technical constraints of the DC network and the devices it comprises. The sparse nonlinear optimization (SNOPT) solution method is employed, comparing its results against those of three recently reported metaheuristic optimization methodologies: the continuous genetic algorithm and the parallel versions of the particle swarm optimizer and the vortex search algorithm. The performance of the proposed methodology is validated on standalone and grid-connected networks, considering the technical and economic conditions of two regions of Colombia (rural and urban territories) and evaluating its effectiveness in terms of solution quality, standard deviation, and processing times. The results show that the SNOPT tool of the General Algebraic Modeling System (GAMS) achieves the global optimum in all test scenarios, exhibiting a standard deviation of zero and requiring less than three seconds on average to generate a solution for an entire day of operation.

Optimal operation of an electricity-hydrogen DC microgrid with integrated demand response

Uncertainties introduced by the high penetration of renewable sources, plug-in-hybrid-electric vehicle load demand, and limited capacity of firm generation available in a DC microgrid make the energy scheduling task rather challenging. This paper proposes a decentralized energy management scheme with a real-time pricing-based demand response implementation for a DC microgrid, considering the sectoral coupling between electricity and hydrogen energy. The objectives of the scheduling strategy are to maximize the profit of the DC microgrid operator and reduce the cost of energy use by the consumers, considering the interaction and interdependence of the electrical and hydrogen systems with a detailed DC microgrid network model and associated network constraints. The DC microgrid operator schedules flexible resources under its control (power procurement from the upstream grid, microturbines, battery energy storage, hydrogen storage, electrolyzer and fuel cell) and sets real-time prices. The consumers set their consumption patterns according to the real-time price. The DC microgrid operator side flexibilities are coordinated with the consumer side flexibilities (thermostatically controlled load like air-conditioner and plug-in-hybrid electric vehicle) using the decentralized “Alternating Direction Method of Multipliers” approach. The probabilistic Copula theory models correlated input uncertainties. Simulation results on a six-bus DC microgrid test system reveal that the operating cost of the DC microgrid operator reduces by ∼11.06% while the energy use cost of consumers reduces by ∼4.80% using the proposed approach for the system under study.

Hierarchical robust Day-Ahead VPP and DSO coordination based on local market to enhance distribution network voltage stability

As distributed energy resources (DER) are increasing, a variety of business models and markets are appearing. Business models such as virtual power plants (VPPs) contribute to enhanced stability of the power system while improving the profits of participating resources. However, the uncertainty of the behavior of DERs and the resulting stability issues are a major barrier to DER deployment and require a coordination framework between grid operators and business models to overcome. Therefore, in this paper, a hierarchical robust day-ahead coordination of a VPP and distribution system operator (DSO) based on the local market is proposed to improve the voltage stability of the distribution network by considering the uncertainty of DERs. For fairness reasons, the contribution of DERs to the distribution system can be addressed by a market-based approach. Local markets are divided into local energy markets (LEM) and local flexibility markets (LFM), which can consider the uncertainty and profitability of DERs. First, the DER determines the seller and buyer status of the LEM based on its self- scheduling with expected prices and is matched based on a double-auction mechanism. At this time, the worst-case scenario of load and renewable energy generation predicted by the Gaussian process (GP) is considered. The mismatched amounts in the LEM are aggregated by the VPP and bid into the wholesale energy market (WEM) operated by the TSO (Transmission system operator). Subsequently, DSO operates a LFM that coordinates microgrid (MG) and VPP on the distribution grid to increase voltage stability. The LFM and WEM problems are considered as interval optimization in the form of mixed integer linear programming and are applied together. The flexibility supply resulting from the adjustment of VPP is compensated by a loss-sensitivity-based flexibility price. Since voltage stability exists in upper and lower bounds, the upper and lower bound of net load through GP-based forecasting is considered along with the linearized network constraints. The results are validated through Monte-Carlo simulation-based scenarios on IEEE benchmark system in MATLAB environment. As a result, it is proved that the proposed model improves the distribution network voltage stability and the profit of the participants.

Introducing storage operators for coordinating residential batteries in distribution networks under time-of-use tariffs and adaptive power limits

The transition towards Time of Use (ToU) tariffs has become a promising solution for addressing power system challenges resulting from increased installations of renewable energy systems. ToU tariffs encourage residential Battery Energy Storage System (BESS) adoption to reduce customers' bills through maximizing energy storage during low-price intervals (e.g., middle of the day). However, simultaneous BESS charging affects diversity of load, which may lead to the violation of distribution networks constraints. Traditional network management with conservative fixed and static power limits leads to inefficient network capacity use since they do not consider changes in network operating conditions and status of BESS facilities. Specially, these approaches do not allow higher import limits when proportion of BESS facilities are in idling state. To better allocate the capacity of distribution networks to active BESS facilities (charging/discharging), this work introduces an independent storage operator to coordinate BESS control actions by employing time-varying and adaptive power limits. For this purpose, a Mixed Integer Linear Programming (MILP) algorithm is proposed for storage operator to manage BESS facilities while respecting network constraints and customers' desired bills. At each time step, the algorithm decides power limits for active BESS facilities based on predefined linear functions. These functions are generated offline by using Optimal Power Flow (OPF) to establish relationships between power limits and number of active BESS. The application of the algorithm using a real Jordanian distribution network demonstrates its effectiveness to allow a larger number of customers achieving their desired bills compared to using fixed power limits.

Near-Optimal Packet Scheduling in Multihop Networks with End-to-End Deadline Constraints

Scheduling packets with end-to-end deadline constraints in multihop networks is an important problem that has been notoriously difficult to tackle. Recently, there has been progress on this problem in the worst-case traffic setting, with the objective of maximizing the number of packets delivered within their deadlines. Specifically, the proposed algorithms were shown to achieve Ω(1/log(L)) fraction of the optimal objective value if the minimum link capacity in the network is C min = Ω(log (L)), where L is the maximum length of a packet's route in the network (which is bounded by the packet's maximum deadline). However, such guarantees can be quite pessimistic due to the strict worst-case traffic assumption and may not accurately reflect real-world settings. In this work, we aim to address this limitation by exploring whether it is possible to design algorithms that achieve a constant fraction of the optimal value while relaxing the worst-case traffic assumption. We provide a positive answer by demonstrating that in stochastic traffic settings, such as i.i.d. packet arrivals, near-optimal, (1-ε)-approximation algorithms can be designed if C min = Ω((over (logL/ε) Ω 2 ). To the best of our knowledge, this is the first result that shows this problem can be solved near-optimally under nontrivial assumptions on traffic and link capacity.

Automated Registration of Full Moon Remote Sensing Images Based on Triangulated Network Constraints

Abstract. The registration of full-moon remote sensing images constitutes a pivotal stage in the fusion analysis of multiple lunar remote sensing datasets. Addressing prevailing issues in automatic registration, such as the broad width of full-moon data, significant internal distortion, and texture distortion in high-latitude regions, this paper proposes a method for automatic matching and correction based on triangulation constraints. The approach employs a matching strategy progressing from coarse to fine and from sparse to dense. It optimizes and combines multiple existing matching algorithms, enhances the extraction of initial network points, constructs irregular triangulation networks using these points, conducts dense matching with each triangulation network as a basic unit, and introduces a geometric correction method based on triangulation network + grid (TIN + GRID) for the registration of full-moon data. For the matching of full-moon remote sensing images in high-latitude regions, a novel approach involving memory projection forward transformation-matching-projection inverse transformation is adopted. Through registration experiments with full-moon image data and an analysis of registration accuracy at different latitudes, the average mean square error is found to be less than 2 pixels. These results signify the efficacy of the proposed method in effectively addressing the automatic registration challenges encountered in full-moon remote sensing images.

Event-Triggered Adaptive Containment Control for Heterogeneous Stochastic Nonlinear Multiagent Systems.

This article investigates the event-triggered adaptive containment control problem for a class of stochastic nonlinear multiagent systems with unmeasurable states. A stochastic system with unknown heterogeneous dynamics is established to describe the agents in a random vibration environment. Besides, the uncertain nonlinear dynamics are approximated by radial basis function neural networks (NNs), and the unmeasured states are estimated by constructing the NN-based observer. In addition, the switching-threshold-based event-triggered control method is adopted with the hope of reducing communication consumption and balancing system performance and network constraints. Moreover, we develop the novel distributed containment controller by utilizing the adaptive backstepping control strategy and the dynamic surface control (DSC) approach such that the output of each follower converges to the convex hull spanned by multiple leaders, and all signals of the closed-loop system are cooperatively semi-globally uniformly ultimately bounded in mean square. Finally, we verify the efficiency of the proposed controller by the simulation examples.

Integration flexibility of renewable distributed energy resources in active distribution networks: A two-module data-driven characterization method

Characterizing the integration flexibility of renewable distributed energy resources (RDERs), which describes the feasible region of RDER accommodations with which the operational constraints of an active distribution network (ADN) can be respected, facilitates the ADN operations. However, traditional characterization methods face the challenge of concurrently dealing with inevitable AC power flow and numerous RDERs. To resolve this issue, this paper develops a novel characterization method that contains two interconnected modules: learning and updating modules. The two modules jointly and iteratively calculate the parameters to characterize the integration flexibility of RDERs (IFR). In the learning module, a new loss function is designed to train the IFR parameters using a training data set. In the updating module, two optimization problems are developed based on the optimization-based performance estimation of the trained parameters to generate more data points for performance improvement in the learning module. The effectiveness of the proposed method is corroborated in the IEEE 33-bus and IEEE 136-bus test systems.

A peer‐to‐peer joint energy and reserve market considering renewable generation uncertainty: A generalized Nash equilibrium approach

AbstractPeer‐to‐peer (P2P) energy trading enhances distribution network resilience by reducing energy demand from central power plants and enabling distributed energy resources to support critical loads after extreme events. However, adequate reserves from main grids are still required to ensure real‐time energy balance in distribution networks due to the uncertainty in renewable generation. This paper introduces a novel two‐stage joint energy and reserve market for prosumers, wherein local flexible resources are fully utilized to manage renewable generation uncertainty. In contrast to cooperative optimization methods, the interactions between prosumers are modelled as a generalized Nash game (GNG), considering that prosumers are self‐interested and should follow distribution network constraints. Then, linear decision rules are employed to ensure a feasible market equilibrium and develop a privacy‐preserving algorithm to guide prosumers toward the market equilibrium with a proven convergence. Finally, the numerical study on a modified IEEE 33‐power system demonstrates that the designed market effectively manages renewable generation uncertainty, and that the algorithm converges to the market equilibrium.

Generalized Nash Equilibrium Analysis for Peer-to-Peer Transactive Energy Market Considering Coupling Distribution Network Constraints

Generalized Nash Equilibrium Analysis for Peer-to-Peer Transactive Energy Market Considering Coupling Distribution Network Constraints

Whale Optimization for Cloud-Edge-Offloading Decision-Making for Smart Grid Services.

As IoT metering devices become increasingly prevalent, the smart energy grid encounters challenges associated with the transmission of large volumes of data affecting the latency of control services and the secure delivery of energy. Offloading computational work towards the edge is a viable option; however, effectively coordinating service execution on edge nodes presents significant challenges due to the vast search space making it difficult to identify optimal decisions within a limited timeframe. In this research paper, we utilize the whale optimization algorithm to decide and select the optimal edge nodes for executing services' computational tasks. We employ a directed acyclic graph to model dependencies among computational nodes, data network links, smart grid energy assets, and energy network organization, thereby facilitating more efficient navigation within the decision space to identify the optimal solution. The offloading decision variables are represented as a binary vector, which is evaluated using a fitness function considering round-trip time and the correlation between edge-task computational resources. To effectively explore offloading strategies and prevent convergence to suboptimal solutions, we adapt the feedback mechanisms, an inertia weight coefficient, and a nonlinear convergence factor. The evaluation results are promising, demonstrating that the proposed solution can effectively consider both energy and data network constraints while enduring faster decision-making for optimization, with notable improvements in response time and a low average execution time of approximately 0.03 s per iteration. Additionally, on complex computational infrastructures modeled, our solution shows strong features in terms of diversity, fitness evolution, and execution time.

A Fault Restoration Method for DC-interconnected Distribution Systems with Renewable Resources Considering Multiple Fault Scenes

Background:: The distribution systems are gradually transformed into active systems with the large-scale integration of renewable resources, which brings greater potential to the fault recovery of the distribution networks and higher requirements for the power scheduling capability under fault conditions. Power electronic devices such as soft open points (SOPs) can break the radial constraints of the traditional distribution network, provide cross-station power support in fault scenarios, and enhance the resilience of the distribution networks. Methods:: This paper focuses on the distribution systems with multi-transformer flexible interconnection, and a multi-device coordinated fault restoration method is proposed. Firstly, the characteristics of different transformer fault scenes and the fault restoration process coordinated by the bus-bar switches and SOPs in each scene are sorted out. Secondly, an optimization model of the fault restoration for DC-interconnected distribution system is established considering the network operation and the equipment constraints in the fault restoration process. Result:: The proposed model aims at the maximization of the weighted load restoration amount, and is transformed into a second-order cone programming model to be solved quickly. Lastly, the effectiveness of the proposed method is verified in a typical flexible interconnection distribution system. Conclusion:: The simulation results prove that it can effectively improve the capability of preserving power supply for important loads after transformer failure.

Grid-optimal energy community planning from a systems perspective

Energy communities (ECs) are a potential enabler for the socio-technical transition to carbon-neutrality. Their emergence depends on setting the right course across various dimensions: the legislative system, social psychology, the economic system, the technological system, and the natural ecosystem. A goal of this research is to identify success factors for energy community projects and their mutual influence in different stages of project development along these dimensions. We demonstrate that the project’s success in the motivation phase depends much more on the mutual influence of the economic system on social psychology compared to the project development or implementation and operation phase. This comprehensive analysis enables planners and policymakers to understand requirements and their role in the promotion of ECs. Another research goal is to understand the impact of different EC planning scenarios on both the EC profitability and the network. In a real network case study, we show that EC planning which considers network constraints can achieve high PV penetration while avoiding critical grid-stability issues. At the same time, the community yields almost the same benefits as in the EC cost-optimal planning scenario. The scenarios partially reflect the before-mentioned dimensions used to analyse and discuss EC planning from a holistic perspective, which reveals potential enablers and barriers for grid-friendly ECs.

DECENTRALIZED QUANTUM COMPILATION FRAMEWORK: EMPOWERING DISTRIBUTED QUANTUM COMPUTING THROUGH MODULARITY

Quantum algorithms require large resources in terms of qubit number, much larger than those available with current NISQ processors. With the network and communication functionalities provided by the Quantum Internet, Distributed Quantum Computing (DQC) is considered as a scalable approach for increasing the number of available qubits for computational tasks. For DQC to be effective and efficient, a quantum compiler must find the best partitioning for the quantum algorithm and then perform smart remote operation scheduling to optimize EPR pair consumption. At the same time, the quantum compiler should also find the best local transformation for each partition. In this paper we present a modular quantum compilation framework for DQC that takes into account both network and device constraints and characteristics. We implemented and tested a quantum compiler based on the proposed framework with some circuits of interest, such as the VQE and QFT ones, considering different network topologies, with quantum processors characterized by heavy hexagon coupling maps. We also devised a strategy for remote scheduling that can exploit both TeleGate and TeleData operations and tested the impact of using either only TeleGates or both. The evaluation results show that TeleData operations may have a positive impact on the number of consumed EPR pairs, while choosing a more connected network topology helps reduce the number of layers dedicated to remote operations. Keywords— Distributed quantum computing (DQC), quantum compilation, quantum Internet

Hydrothermal–pumped-storage scheduling considering renewable energy uncertainty by improved cheetah optimization

The growth of renewable wind and solar energy in modern power systems affects the short-term scheduling of hydrothermal power generation. This article presents the optimum day-ahead scheduling of wind–solar–hydrothermal systems with pumped-storage plants (PSPs). To assess the contribution of PSPs in decreasing the generation costs of a renewable integrated power system, a scheduling model is formulated that considers different security constraints. An improved cheetah optimizer (ICO) is implemented on a renewable integrated test power system to solve the short-term optimum wind–solar–pumped-storage plant–hydrothermal scheduling (OWSPHTS) problem, taking into account all thermal, hydraulic and network constraints. Optimum solutions are obtained with different objectives considering renewable uncertainties. The proposed ICO solution method is compared with the similar algorithms in terms of optimal fuel costs, emissions, convergence success rate and computation time. The comparative solutions show that the introduced optimization technique is efficient for solving the real-world OWSPHTS problem.

Implementasi Model Blended Learning pada Mata Pelajaran Matematika dengan Media Whatsapp Peserta Didik di Sekolah Dasar

Blended learning is a learning model that combines face-to-face learning with online learning. After the end of Covid-19, teachers continued to implement technology-based learning using WhatsApp media to introduce technology without abandoning face-to-face learning. This research aims to describe the implementation of blended learning models in mathematics subjects using WhatsApp media for students in elementary schools. This research uses a qualitative approach. Research conducted by researchers at Depok 1 State Elementary School on class IV students totaling 27 students. Data collection was obtained through interviews and observations. The research results showed that interactions that occurred via WhatsApp media facilitated discussions between teachers and students outside of face-to-face learning time. Planning is done by selecting materials, implementation is done by sending materials via WhatsApp, and evaluation is done by giving tests. Apart from that, WhatsApp also has obstacles, namely network constraints, support factors and parental control while in the home environment. The implications of the blended learning model using WhatsApp media are appropriate to apply, because WhatsApp is a familiar media and is one of the media that is easy to use. Technological developments are increasingly rapid, so teachers can introduce technology for education to students.