Network Operational Costs Research Articles

Dedicated Microgrid Planning and Operation Approach for Distribution Network Support With Pumped-Hydro Storage

This study presents a dedicated microgrid planning and operation approach for distribution network support considering pumped-hydro storage. The term dedicated connotes the service responsibility of the microgrid rather than its operational independence. A non-dedicated microgrid has a dual responsibility to serve the distribution system and commercial/industrial consumers. The payback period and lifetime benefit of a non-dedicated microgrid partly rely on the time-of-use pricing mechanism which drives the microgrid to maximize profit, and could compromise the overall operation of the distribution network. This paper develops an approach for engaging dedicated microgrids having full obligation to serve the distribution system only. For effectively managing the operation of the microgrid without jeopardizing the operation of the distribution network, the approach considers a novel short-term operation capacity index, power purchase agreement, and levelized energy cost. Specific functionalities of pumped-hydro storages are used to relax the microgrids sensitivity to operation penalty arising from output power deviation, and thus allowing the microgrid to increase profits. The results obtained showed mutual benefits for the system operator and microgrid owner since the distribution network operation cost was reduced and the microgrid owner increased its revenue.

Multi-timescale interaction of a coupled electricity-hydrogen-gas system with a distribution network considering dynamic games

The large-scale access of new energy sources to distribution networks has led to its safe and stable operation. Therefore, this study considers a multi-timescale interaction of a coupled electricity-hydrogen-natural gas (EHG) system with a distribution grid for dynamic games. First, a multifaceted interaction mechanism between the coupling system and the distribution network balancing service mechanism, demand response service mechanism, and frequency regulation service mechanism was designed. Furthermore, an interaction framework between the coupled system and the distribution network was proposed, and a three-level time-scale interaction strategy between the distribution network and the coupled system was constructed considering the dynamic game in the medium- and long-term, short-term, and ultrashort terms. Subsequently, the presence and uniqueness of the interactive equilibrium solution of the dynamic game was proven. Finally, a region was used as an example for arithmetic analysis. The results of the calculation example indicate the following: 1) the ranges of interaction deviation under the medium-to long-term, short-term, and ultra-short-term time scales are [-3.76%, 2.84%], [-2.37%, 1.43%], and [-0.43%, 0.87%], respectively, indicating that the interaction deviation between the coupling system and the distribution grid can be reduced via adjusting and correcting the medium-to long-term, short-term, and ultra-short-term time scales. 2) Compared with the scenario in which only a single service is provided, the distribution network operation cost in the present study decreased by 15.92% and the revenue increased by 30.59%, signifying that the coupling system and distribution network diversification mechanism can expand revenue channels. 3) Compared with Scenario 1, without considering the cost of energy storage loss, and Scenario 3, with a single energy storage configuration, the actual service life of the battery in this study was the longest, 23.72% longer than Scenario 1, and 47.90% longer than Scenario 3, revealing that hybrid energy storage can achieve coordinated optimization and optimal allocation of resources at different time scales. 4) The interaction between the distribution network and coupling system, considering dynamic games, can form a fair price and guide to improved interaction between the distribution network and coupling system.

Evaluation for Risk of Cascading Failures in Power Grids by Inverse-Community Structure

Recently, the development of the Internet of Things (IoT) enables more comprehensive and intelligent analysis and defense for cascading failures in power grids. This paper summarizes power grids as Temporal Weighted Networks (TWN) which are different from conventional temporal networks. For TWN, the topological structure is fixed but weight distribution is time-varying. Then it is noted that for different operating states represented by different weight (power flow) distribution at different time sections, the risks of cascading failures would be completely different. Inspired by analysis of inter-subnetworks power shifts in cascading failures, Inverse-Community (IC) structure is proposed in TWN to intuitively identify the risk of cascading failures. IC describes a structure in weighted networks with several communities in which the weighted interaction between communities is significantly stronger than that within the same community. Furthermore, the conventional modularity is upgraded as Inverse Modularity (IM) to quantify the characteristic of IC structure in power networks. Subsequently, considers the risk of cascading failures represented by IM and the cost of power network operation, a security/economic dispatch (SED) method is designed to handle the optimal power dispatch issues. Simulation results prove a positive correlation between IM of power flow distribution and risk of cascading failures. Furthermore, the results on the IEEE 118-bus system demonstrate the effectiveness of the proposed SED method in mitigating cascading failure risks.

Schemes for Extending the Network Lifetime of Wireless Rechargeable Sensor Networks

Most Wireless Rechargeable Sensor Networks (WRSN) allow simultaneous energy replenishment with data gathering. These schemes suffer from high energy consumption of mobile chargers and could require new sensor design for their integration. Thus, we propose Distributed Energy Replenishment and Data Gathering (DERDG) to reduce the energy consumption of the chargers and Spatio-Temporal Non-Concurrent Data gathering and Energy replenishment (ST-NCDR) that are easily be integrable on the sensor nodes. In these schemes, sensor nodes are divided into clusters and requests from energy-hungry nodes are arranged based on their temporal properties in DERDG, but using their spatial and temporal properties in ST-NCDR. In ST-NCDR scheme, two mobile chargers replenish the energy of energy-hungry nodes when they are not performing sensing and transmission of data, but irrespective of their operational states in DERDG scheme. Simulation results indicate the superiority of the schemes over state-of-the-art schemes in terms of energy consumption of mobile chargers and average residual energy of the nodes. Both DERDG and ST-NCDR reduced the energy consumption of the chargers, without reduction in the network lifetime, by an average of 29.45% and 73.70%, respectively, when compared to the work of Han et al. (2018), and by 71.40% and 93.80% in comparison to the work of Mikail et al. (2020). DERDG reduces data delivery delay by 95.50% in comparison to the work of Han et. al, (2018). The findings imply that ST-NCDR can be easily integrated into sensor nodes and yields a reduction in the energy of mobiles chargers use in charging the sensor nodes that translate to lower cost of network operation, in addition to improving the residual energy of nodes in WRSN. This part should state the context of the research being reported. Aim: the objective of the research should be clearly stated here Method: the research approach used in the study, justifying its suitability for the study should be stated here. Results: the findings and their implication(s) should be clearly enumerated and briefly discussed here

The potential of lake-source district heating and cooling for European buildings

Lake-source thermal district networks can efficiently supply heating and cooling to buildings and thus save energy and CO2 emissions. However, it remains unclear to which degree they are a sustainable alternative at a larger geographical scale. An evaluation of the potential of developing technically and economically feasible lake-source district systems in Europe was performed in this study, with an integrated spatial explicit techno-economic assessment that accounts for different boundary conditions, such as electricity price, CO2 price and climate change. The feasibility of covering building energy demand near lakes was found to be particularly sensitive to the relationship between capital costs from network design and operational costs from heat pumps, associated electricity consumption and CO2 emissions. Results suggest a European techno-economic potential of 1.9 TWh/y considering only direct cooling and 11.3 TWh/y if thermal networks supply both direct heating and cooling by heat pumps. Respective electricity savings are 0.36 TWh/y and 0.78 TWh/y. An estimated 17% of the cooling demand near European lakes can thus be covered by viable cooling-only lake-source systems. For combined systems, the viable potential is estimated to be 7% of the total combined heating and cooling demand. Lake-source district systems are found to be particularly promising for Italy, Germany, Turkey and Switzerland. The integration of lake-source thermal networks should rarely lead to severe lake water temperature alteration and therefore not limit the techno-economic potential. The introduced methodology allows for a combined evaluation of technological, ecological and economic boundary conditions for using lakes as a source for district heating and cooling. Thereby, a more realistic estimation of their potential implementation becomes possible, enabling informed energy planning for central or decentral system configurations.

Coordinated active and reactive power operation of multiple dispersed resources for flexibility improvement

The purpose of this paper is to reach the optimal active and reactive power operation of multiple dispersed resources consisting of mobile energy storage system (MESS), demand response (DR) and photovoltaic (PV), for flexibility improvement of distribution network with uncertain PV and DR, minimization of power loss and operation cost whilst satisfaction of both power factor and voltage variation requirement. Especially, the flexibility aspect of distribution network is focused due to its significance for supporting economic operation without voltage rise issue during high PVs integration. Firstly, the active and reactive power operation spaces of MESS and PV inverter are discussed under power factor constraint. Then, the stochastic characteristics of PV generation and DR of microgrids are investigated using probability distribution. After that, the optimization framework coordination with dispersed MESS, PV inverter and DR to ensure operational flexibility of distribution network is proposed. Finally, the total cost minimization based flexibility improvement approach is presented by optimizing power loss, uncertain risk, operation cost of distribution network and MESS, satisfying operation constraints of both distribution network and dispersed resources. Simulation results conducted on the IEEE 69-bus system demonstrate the effectiveness of the proposed approach for PV accommodation, voltage quality improvement as well as peak load shaving.

Dependable modulation classifier explainer with measurable explainability.

The Internet of Things (IoT) plays a significant role in building smart cities worldwide. Smart cities use IoT devices to collect and analyze data to provide better services and solutions. These IoT devices are heavily dependent on the network for communication. These new-age networks use artificial intelligence (AI) that plays a crucial role in reducing network roll-out and operation costs, improving entire system performance, enhancing customer services, and generating possibilities to embed a wide range of telecom services and applications. For IoT devices, it is essential to have a robust and trustable network for reliable communication among devices and service points. The signals sent between the devices or service points use modulation to send a password over a bandpass frequency range. Our study focuses on modulation classification performed using deep learning method(s), adaptive modulation classification (AMC), which has now become an integral part of a communication system. We propose a dependable modulation classifier explainer (DMCE) that focuses on the explainability of modulation classification. Our study demonstrates how we can visualize and understand a particular prediction made by seeing highlighted data points crucial for modulation class prediction. We also demonstrate a numeric explainability measurable metric (EMM) to interpret the prediction. In the end, we present a comparative analysis with existing state-of-the-art methods.

Energy efficient resource allocation method for 5G access network based on reinforcement learning algorithm

Edge computing and IIoT (Industrial Internet of Things) are two representative application scenarios in 5G (5th Generation) mobile communication technology network. Therefore, this paper studies the resource allocation methods for these two typical scenarios, and proposes an energy-efficient resource allocation method by using DRL algorithm, which enhances the network performance and reduces the network operation cost. Firstly, according to the characteristics of edge computing, taking the connection relationship between base stations, users and the transmission power allocated by base stations to users as decision variables, minimizing the overall energy efficiency as the goal, and taking the needs of mobile users as constraints, a resource allocation model for user quality of service (QoS) guarantee is designed. Secondly, simulation experiments are used to verify that the proposed method has faster training speed and convergence speed, ensures the quality-of-service requirements of mobile users, and realizes intelligent and energy-saving resource allocation. The approximate amounts of steps to convergence under four cases are 500, 350, 250, and 220, respectively, and the values of awards are 21.76, 21.09, 20.38, and 20.25, respectively. Thirdly, aiming at the IIoT environment scenario, this paper proposes a resource allocation method for 5G wide connection low delay services based on asynchronous dominant action evaluation algorithm (A3C). Firstly, according to the characteristics of IIoT environment, taking the connection relationship between base stations and users and the transmission power allocated by base stations to users as decision variables and maximizing energy efficiency while satisfying needs of each user as optimization goal, a resource allocation model for 5G wide connection low delay services is designed. On this basis, an energy-efficient resource allocation method according to A3C is proposed. The hierarchical aggregation clustering algorithm (HAC) is adopted to determine the connection relationship between the base station and users, and the A3C algorithm is adopted to allocate transmission power to users, so as to maximize the overall energy efficiency and ensure the needs of each user.

Energy Storage Configuration of Distribution Networks Considering Uncertainties of Generalized Demand-Side Resources and Renewable Energies

With the growing proportion of advanced metering infrastructures and intelligent controllable equipment in power grids, demand response has been regarded as an effective and easily implemented approach to meet the demand–supply equilibrium. This paper innovatively proposes generalized demand-side resources combining the demand response with an energy storage system and constructs a configuration model to obtain scheduling plans. Firstly, this paper analyzes the characteristics of generalized demand-side resources and models the translational loads, reducible loads and energy storage system. Secondly, a deterministic energy storage configuration model aiming at achieving the lowest operation cost of distribution networks is established, from which the scheduling scheme of generalized demand-side resources can be obtained. Then, the fuzzy membership function and the probability density function are used to represent the uncertainty of the demand response, the prediction error of renewable energy output and the generalized demand-side resources that do not participate in the demand response. Therefore, this paper simulates daily operations to modify the capacity of energy storage. The problem is solved by using Monte Carlo simulation, fuzzy chance-constrained programming and mixed-integer programming. Finally, the effectiveness of this model is demonstrated with case studies in a 33-node distribution network. The results show that the uncertainty of this system is solved effectively. When only considering generalized demand-side resources, the total cost is reduced by 9.5%. After considering the uncertainty, the total cost is also decreased 0.3%. Simultaneously, the validity of the model is verified.

A comprehensive optimal power and gas flow in multi-carrier energy networks in the presence of energy storage systems considering demand response programs

• Presenting a complete model of an EH contains RES to investigate the OPGF problem. • Modeling the operation limits of CHP, and ramp rate of generators. • Considering demand response programs for both electrical and thermal loads. • Analyzing several EHs, allocated in various buses to minimize the operation costs. • Applying the GAMS software to modified integrated power and gas IEEE14-bus system. Energy hubs (EHs) can help connect different types of energy and create a multi-carrier energy network (MCEN), thereby promoting energy network operation flexibility. Due to the connection of different types of energy in these networks, reduction of operating costs has become a controversial challenge. Herein, the optimal power and gas flow (OPGF) problem in an MCEN was investigated in the presence of wind and solar power sources, electrical and thermal energy storage (EES and TES), and electrical and thermal demand response programs (EDRP and TDRP). In the mentioned EHs, the operating costs of electric and gas networks were minimized, and the operation of EHs was simultaneously investigated. All constraints of the electric and gas networks and different equipment, including combined heat and power (CHP) units, were also considered. The simulation results in GAMS showed that the simultaneous optimization of the operation cost of the case study network, including 14 electric buses and 9 gas buses, during a 24-hour period was reduced up to 4%. Also, due to the lower price of gas, its consumption has increased by about 5%, and this ultimately decreases the total cost of optimal energy distribution of the network compared to the case in which the mentioned strategies and programs were not implemented.

Municipal water supplies efficiencies in Gaza Strip: a data envelopment analysis approach

This paper reports the findings of a research carried out to evaluate the efficiency of municipal water supply systems using Data Envelopment Analysis (DEA). Input and output variables needed for DEA were identified using literature review and questionnaires. Input variables considered in this paper include Number of Connections (NC), Length of Water Network (LWN), Number of Employees (NE) and Maintenance and Operation Costs (MOC), while Total Revenues (TR) and Number of People Served (NPS) were used as output variables. Values of these variables were obtained for the existing 25 municipalities for the years of 2015, 2016 and 2017 and the averages of these variables were calculated and used in the model. The paper uses Charnes, Cooper and Rhodes (CCR) model and Banker, Chames and Cooper (BCC) model. Results indicate that length of water network and maintenance and operation costs were the major sources of inefficiencies.

Blockchain-Based Market Procurement of Reactive Power

A flexible and adaptable market procurement of reactive power has the potential to improve network efficiency, voltage stability, and operational costs. In Europe, such a dynamic procurement must be in accordance with the European Union directive EU 2019/944 on non-frequency ancillary services. In accordance with this situation, a blockchain-based framework of market procurement of reactive power is proposed and presented in this paper. The devised framework is in alignment with the European Union’s directive to establish a non-discriminatory, transparent, and free market. Moreover, the blockchain-based framework is applicable at all network levels and allows for the variety of different distributed resources to participate. A two-layer blockchain topology is proposed to overcome scalability and transaction time issues. The first main-layer blockchain acts as the agent for trust and guarantees the immutability of data as well as the remuneration of participating market players. The second-layer blockchain facilitates direct access to tendering processes or auctions, fast transactions, and low transaction fees for involved stakeholders. Additionally, a decentralized oracle network is proposed to integrate external data into the second-layer blockchain. Data required for verifying the physical reactive power transactions are delivered by smart meters. The entire procurement process is automated by deploying smart contracts at the various blockchain layers. Thus, in principle, the involved stakeholders are the system operators and market players. For the purpose of validation, the holistic market procurement process is demonstrated and analyzed in a hardware-in-the-loop environment involving reactive power procurement at the distribution network level.

Joint Planning of Network Slicing and Mobile Edge Computing: Models and Algorithms

Multi-access Edge Computing (MEC) facilitates the deployment of critical applications with stringent QoS requirements, latency in particular. This paper considers the problem of jointly planning the availability of computational resources at the edge, the slicing of mobile network and edge computation resources, and the routing of heterogeneous traffic types to the various slices. These aspects are intertwined and must be addressed together to provide the desired QoS to all mobile users and traffic types still keeping costs under control. We formulate our problem as a mixed-integer nonlinear program (MINLP) and we define a heuristic, named Neighbor Exploration and Sequential Fixing (NESF), to facilitate the solution of the problem. The approach allows network operators to fine tune the network operation cost and the total latency experienced by users. We evaluate the performance of the proposed model and heuristic against two natural greedy approaches. We show the impact of the variation of all the considered parameters (viz., different types of traffic, tolerable latency, network topology and bandwidth, computation and link capacity) on the defined model. Numerical results demonstrate that NESF is very effective, achieving near-optimal planning and resource allocation solutions in a very short computing time even for large-scale network scenarios.

Ultra-Reliable Distributed Cloud Network Control With End-to-End Latency Constraints

We are entering a rapidly unfolding future driven by the delivery of real-time computation services, such as industrial automation and augmented reality, collectively referred to as augmented information (AgI) services, over highly distributed cloud/edge computing networks. The interaction intensive nature of AgI services is accelerating the need for networking solutions that provide strict latency guarantees. In contrast to most existing studies that can only characterize average delay performance, we focus on the critical goal of delivering AgI services ahead of corresponding deadlines on a per-packet basis, while minimizing overall cloud network operational cost. To this end, we design a novel queuing system able to track data packets’ lifetime and formalize the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">delay-constrained least-cost dynamic network control problem</i> . To address this challenging problem, we first study the setting with average capacity (or resource budget) constraints, for which we characterize the delay-constrained stability region and design a throughput-optimal control policy leveraging Lyapunov optimization theory on an equivalent virtual network. Guided by the same principle, we tackle the peak capacity constrained scenario by developing the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reliable cloud network control</i> (RCNC) algorithm, which employs a two-way optimization method to make actual and virtual network flow solutions converge in an iterative manner. Extensive numerical results show the superior performance of the proposed control policy compared with the state-of-the-art cloud network control algorithm, and the value of guaranteeing strict end-to-end deadlines for the delivery of next-generation AgI services.

Comprehensive configuration strategy of energy storage allocation and line upgrading for distribution networks considering a high proportion of integrated photovoltaics

AbstractThe rapid development of photovoltaics (PVs) and load caused a significant increase in peak loads and peak‐valley differences in rural distribution networks, which require load peak shifting and line upgrading. Large peak‐valley differences also bring challenges on the safe operation of the utility power grid. Considering the integration of a high proportion of PVs, this study establishes a bilevel comprehensive configuration model for energy storage allocation and line upgrading in distribution networks, which can reduce peak loads and peak‐valley differences. In the upper level, a minimum annual planning cost is obtained by developing the installation capacity of centralised energy storage in transformer stations, the installation location and capacity of decentralised energy storage on lines and a scheme of line upgrading. In the lower level, the minimum total annual operation cost of the distribution network is obtained by developing an optimal scheduling for the centralised energy storage in transformer stations and decentralised energy storage on lines. Simulation results show that compared with the conventional energy storage planning strategy, the configuration investment can be reduced by 467.66 million yuan at least with the proposed strategy.

Simultaneous Planning of the Medium and Low Voltage Distribution Networks under Uncertainty: A Bi-Level Optimization Approach

Distribution network expansion planning (DNEP) is one of the important matters in the field of planning and operation of electrical power systems. Since many costs and losses have occurred in the distribution networks, it has increased attention towards this network. The electrical energy distribution network is divided into two parts: the medium voltage (MV) network and the low voltage (LV) network. The main problem in this field is that planning is done either only on the MV network or only on the LV network. While planning in each of these networks has a significant effect on the other networks, this important case has not been considered in most research studies. Therefore, this study has tried to do integrated planning in the form of a bi-level model in the presence of different types of distributed generations (DGs) and consider the uncertainties of renewable sources and load demand in both MV and LV networks so that the planning and operation costs are minimized. In the proposed bi-level model, the upper-level section aims to minimize the investment and the operation cost of the MV network, and the lower-level problem minimizes the investment and the operation cost of the LV network considering the DGs and pollution emission. The obtained results show the effectiveness of the proposed model.

Application of Deep Convolutional Neural Network for Automatic Detection of Digital Optical Fiber Repeater.

The digital optical fiber repeater (DOFR) is an important infrastructure in the LTE networks, which solve the problem of poor regional signal quality. Various types of conventional measurement data from the LTE network cannot indicate whether a working DOFR is present in the cell. Currently, the detection of DOFRs relies solely on maintenance engineers for field detection. Manual detection methods are not timely or efficient, because of the large number and wide geographical distribution of DOFRs. Implementing automatic detection of DOFR can reduce the maintenance cost for mobile network operators. We treat the DOFR detection problem as a classification problem and employ a deep convolutional neural network (DCNN) to tackle it. The measurement report (MR) we used in this paper are tabular data, which is not an ideal input for DCNN. We propose a novel MR representation method that takes the overall MR data of a cell as a sample rather than a single record in the table, and represents the MR data as a pseudo-image matrix (PIM). The PIM will be used as the input for training DCNN, and the trained DCNN will be used to perform DOFR detection tasks. We conducted a series of experiments on real MR data, and the classification accuracy can achieve 93%. The proposed AI-based method can effectively detect the DOFR in a cell.

Trade-offs between Risk and Operational Cost in SDN Failure Recovery Plan

We consider the problem of SDN flow optimization in the presence of a dynamic probabilistic link failures model. We introduce a metric for path risk, which can change dynamically as network conditions and failure probabilities change. As these probabilities change, the end-to-end path survivability probability may drop, i.e., its risk may rise. The main objective is to reroute at-risk end-to-end flows with the minimum number of flow operation so that a fast flow recovery is guaranteed. We provide various formulations for optimizing network risk versus operational costs and examine the trade-offs in flow recovery and the connections between operational cost, path risk, and path survival probability. We present our suboptimal dynamic flow restoration methods and evaluate their effectiveness against the Lagrangian relaxation approach. Our results show a significant improvement in operational cost against a shortest-path approach.

Hypergraph-Based Autonomous Networks: Adaptive Resource Management and Dynamic Resource Scheduling

As development steadily advances and the capacity for practical applications grows, 5G networks are being deeply expanded in many fields, such as manufacturing, transportation and logistics, and agriculture. The rapid growth in the number of devices in application deployments is causing manual methods of managing resources to become inefficient and network operation costs to increase significantly. Therefore, autonomous networks framework that can adapt to dynamic network environments need to be designed to undertake network resource management. This paper investigates the characteristics of 5G networks, and a hypergraph-based 5G autonomous networks framework is proposed to achieve stable interconnection within the system. First, the network topology is accurately represented by introducing the concept of hypergraph, and an RL algorithm is used to optimize the management of network resources based on the network topology. Then, a BERT model is adopted for resource state awareness from the user satisfaction perspective, and a fuzzy decision based collaborative resource scheduling algorithm is designed to improve service quality. Finally, the key challenges that will still be faced and need to be further solved in the future development of 5G autonomous networks are deeply explored.

Dynamic Service Migration and Request Routing for Microservice in Multicell Mobile-Edge Computing

Mobile-edge computing (MEC) sinks computation and storage capacities to network edge, where it is close to users to support delay-sensitive services. However, due to the dynamic and stochastic properties of MEC networks, the deployed services may be frequently migrated among edge servers to follow the mobility of users, which greatly increases the network operational cost. In this article, considering the service migration cost brought by user mobility, we study the joint optimization problem of service deployment and request routing decisions to maximize the long-term network utility of MEC networks. First, we propose a Lyapunov optimization-based online service migration algorithm to decompose the continuous optimization problem into a number of one-slot online optimization problems. Then, to address the NP-hard issue of one-slot optimization, we use a randomized rounding technique to implement service migration and request routing. Furthermore, through a closed-form theoretical analysis, we prove that the proposed algorithm not only greatly meets the local user requests and enables approximate performance guarantees but also adaptively balances the service migration cost and system performance online. Finally, extensive simulations are conducted, which demonstrate that our algorithm can efficiently utilize the storage and computation resources of edge servers, and maximize the long-term network utility while ensuring the stability of service migration cost.