Records with Subject: Planning & Scheduling
Showing records 1 to 25 of 122. [First] Page: 1 2 3 4 5 Last
A Fuzzy Multicriteria Decision-Making (MCDM) Model for Sustainable Supplier Evaluation and Selection Based on Triple Bottom Line Approaches in the Garment Industry
Chia-Nan Wang, Ching-Yu Yang, Hung-Chun Cheng
September 5, 2019 (v1)
Keywords: FAHP, fuzzy logics, garment industry, Optimization, supplier selection, TOPSIS, triple bottom line
Vietnam’s garment industry is facing many challenges, including domestic competition and the global market. The free trade agreement, which Vietnam signed, includes environmental barriers, sustainable development, and green development. The agreement further requires businesses to make efforts to improve not only product quality but also the production process. In cases when enterprises cause environmental pollution in the production process and do not apply solutions to reduce waste, save energy, and natural resources, there is a risk of no longer receiving orders or orders being rejected, especially orders from the world’s major branded garment companies. In this research, the authors propose a multicriteria decision-making model (MCDM) for optimizing the supplier evaluation and selection process for the garment industry using sustainability considerations. In the first stage of this research, all criteria affecting supplier selection are determined by a triple bottom line (TBL) mode... [more]
Development of a Two-Stage ESS-Scheduling Model for Cost Minimization Using Machine Learning-Based Load Prediction Techniques
Minsu Park, Jaehwi Kim, Dongjun Won, Jaehee Kim
August 14, 2019 (v1)
Keywords: bagging, energy storage system, ensemble learning, load prediction, Machine Learning, random forest, two-stage algorithm
Effective use of energy storage systems (ESS) is important to reduce unnecessary power consumption. In this paper, a day-ahead two-stage ESS-scheduling model based on the use of a machine learning technique for load prediction has been proposed for minimizing the operating cost of the energy system. The proposed algorithm consists of two stages of ESS. In the first stage, ESS is used to minimize demand charges by reducing the peak load. Then, the remaining capacity is used to reduce energy charges through arbitrage trading, thereby minimizing the total operating cost. To achieve this purpose, accurate load prediction is required. Machine learning techniques are promising methods owing to the ability to improve forecasting performance. Among them, ensemble learning is a well-known machine learning method which helps to reduce variance and prevent overfitting of a model. To predict loads, we employed bootstrap aggregating (bagging) or random forest technique-based decision trees after Ho... [more]
Productivity Models of Infill Complex Structural Wells in Mixed Well Patterns
Liang Sun, Baozhu Li, Yong Li
August 5, 2019 (v1)
Keywords: complex structural well, mixed well pattern, productivity evaluation, semi-analytical model, well location optimization
The mathematical models of productivity calculation for complex structural wells mainly focus on the single well or the regular well pattern. Previous research on the seepage theory of complex structural wells and vertical wells in mixed well pattern is greatly insufficient. Accordingly, this article presents a methodology of evaluating the productivity of infill complex structural wells in mixed well patterns. On the basis of the mirror-image method and source−sink theory, two semi-analytical models are established. These models are applied to the productivity prediction of an infill horizontal well inhorizontal-vertical well pattern and an infill multilateral well inmultilateral-vertical well pattern, respectively, in which the interference of other wells, the randomicity of well patterns, and the pressure drawdown along the horizontal laterals are taken into account. The semi-analytical models’ results are consistent with those calculated by the Eclipse reservoir simulator with the... [more]
A Scenario-Based Optimization Model for Planning Sustainable Water-Resources Process Management under Uncertainty
Hongchang Miao, Donglin Li, Qiting Zuo, Lei Yu, Xiaoxia Fei, Lingang Hao
July 31, 2019 (v1)
Keywords: programming, scenario analysis, South-to-North Water Diversion Project of China, uncertainty, water-resources process management
Discrepancies between water demand and supply are intensifying and creating a need for sustainable water resource process management associated with rapid economic development, population growth, and urban expansion. In this study, a scenario-based interval fuzzy-credibility constrained programming (SIFCP) method is developed for planning a water resource management system (WRMS) that can handle uncertain information by using interval values, fuzzy sets, and scenario analysis. The SIFCP-WRMS model is then applied to plan the middle route of the South-to-North Water Diversion Project (SNWDP) in Henan Province, China. Solutions of different water distribution proportion scenarios and varied credibility levels are considered. Results reveal that different water-distribution proportion scenarios and uncertainties used in the SIFCP-WRMS model can lead to changed water allocations, sewage discharges, chemical oxygen demand (COD) emissions, and system benefits. Results also indicate that the... [more]
An Improved Compact Genetic Algorithm for Scheduling Problems in a Flexible Flow Shop with a Multi-Queue Buffer
Zhonghua Han, Quan Zhang, Haibo Shi, Jingyuan Zhang
July 31, 2019 (v1)
Keywords: flexible flow shop scheduling, improved compact genetic algorithm, multi-queue limited buffers, probability density function of the Gaussian distribution
Flow shop scheduling optimization is one important topic of applying artificial intelligence to modern bus manufacture. The scheduling method is essential for the production efficiency and thus the economic profit. In this paper, we investigate the scheduling problems in a flexible flow shop with setup times. Particularly, the practical constraints of the multi-queue limited buffer are considered in the proposed model. To solve the complex optimization problem, we propose an improved compact genetic algorithm (ICGA) with local dispatching rules. The global optimization adopts the ICGA, and the capability of the algorithm evaluation is improved by mapping the probability model of the compact genetic algorithm to a new one through the probability density function of the Gaussian distribution. In addition, multiple heuristic rules are used to guide the assignment process. Specifically, the rules include max queue buffer capacity remaining (MQBCR) and shortest setup time (SST), which can i... [more]
Multi-Objective Optimal Scheduling Method for a Grid-Connected Redundant Residential Microgrid
Weiliang Liu, Changliang Liu, Yongjun Lin, Kang Bai, Liangyu Ma, Wenying Chen
July 31, 2019 (v1)
Keywords: analytic hierarchy process (AHP), non-dominate sorting genetic algorithm II (NSGA-II), optimal scheduling, redundant residential microgrid (RR-microgrid), virtual energy storage system (VESS)
Optimal scheduling of a redundant residential microgrid (RR-microgrid) could yield economical savings and reduce the emission of pollutants while ensuring the comfort level of users. This paper proposes a novel multi-objective optimal scheduling method for a grid-connected RR-microgrid in which the heating/cooling system of the RR-microgrid is treated as a virtual energy storage system (VESS). An optimization model for grid-connected RR-microgrid scheduling is established based on mixed-integer nonlinear programming (MINLP), which takes the operating cost (OC), thermal comfort level (TCL), and pollution emission (PE) as the optimization objectives. The non-dominate sorting genetic algorithm II (NSGA-II) is employed to search the Pareto front and the best scheduling scheme is determined by the analytic hierarchy process (AHP) method. In a case study, two kinds of heating/cooling systems, the radiant floor heating/cooling system (RFHCS) and the convection heating/cooling system (CHCS) ar... [more]
Bidding Strategy for Aggregators of Electric Vehicles in Day-Ahead Electricity Markets
Yunpeng Guo, Weijia Liu, Fushuan Wen, Abdus Salam, Jianwei Mao, Liang Li
July 26, 2019 (v1)
Keywords: bidding strategy, economic dispatch, electric vehicle (EV), electric vehicle aggregator, electricity market
To make full use of the flexible charging and discharging capabilities of the growing number of electric vehicles (EVs), a bidding strategy for EV aggregators to participate in a day-ahead electricity energy market is proposed in this work. The proposed bidding strategy is able to reduce the operating cost of the EV aggregators and to handle the uncertainties of day-ahead market prices properly at the same time. Agreements between the EV owners and the aggregators are discussed, and a hierarchical market structure is proposed. While assuming the aggregators as economic rational entities, the bidding strategy is established based on the market prices, extra battery charging/discharging costs and the expected profits. The bidding clearing system will display the current/temporal market clearance results of the day-ahead market before the final clearance, and hence the market participants can revise their bids and mitigate the risks, to some extent, of forecasted market price forecast err... [more]
Stochastic and Deterministic Unit Commitment Considering Uncertainty and Variability Reserves for High Renewable Integration
Ilias G. Marneris, Pandelis N. Biskas, Anastasios G. Bakirtzis
July 26, 2019 (v1)
Keywords: deterministic programming, multi-timing scheduling, real-time dispatch, stochastic programming, uncertainty reserve, variability reserve, wind integration
The uncertain and variable nature of renewable energy sources in modern power systems raises significant challenges in achieving the dual objective of reliable and economically efficient system operation. To address these challenges, advanced scheduling strategies have evolved during the past years, including the co-optimization of energy and reserves under deterministic or stochastic Unit Commitment (UC) modeling frameworks. This paper presents different deterministic and stochastic day-ahead UC formulations, with focus on the determination, allocation and deployment of reserves. An explicit distinction is proposed between the uncertainty and the variability reserve, capturing the twofold nature of renewable generation. The concept of multi-timing scheduling is proposed and applied in all UC policies, which allows for the optimal procurement of such reserves based on intra-hourly (real-time) intervals, when concurrently optimizing energy and commitments over hourly intervals. The day-... [more]
Optimal Expansion Co-Planning of Reconfigurable Electricity and Natural Gas Distribution Systems Incorporating Energy Hubs
Xianzheng Zhou, Chuangxin Guo, Yifei Wang, Wanqi Li
July 26, 2019 (v1)
Keywords: electricity and natural gas distribution systems, energy hub, Energy Storage, expansion planning, multi-energy systems, reconfiguration
In a carbon-constrained world, natural gas with low emission intensity plays an important role in the energy consumption area. Energy consumers and distribution networks are linked via energy hubs. Meanwhile, reconfiguration that optimizes operational performance while maintaining a radial network topology is a worldwide technique in the electricity distribution system. To improve the overall efficiency of energy infrastructure, the expansion of electricity distribution lines and elements within energy hubs should be co-planned. In this paper, the co-planning process is modeled as a mixed integer quadratic programming problem to handle conflicting objectives simultaneously. We propose a novel model to identify the optimal co-expansion plan in terms of total cost. Operational factors including energy storages and reconfiguration are considered within the systems to serve electricity, cooling and heating loads. Reconfiguration and elements in energy hubs can avoid or defer new elements’... [more]
Efficient Energy Consumption Scheduling: Towards Effective Load Leveling
Yuan Hong, Shengbin Wang, Ziyue Huang
July 26, 2019 (v1)
Keywords: demand response, demand side management, load leveling, Scheduling, smart grid
Different agents in the smart grid infrastructure (e.g., households, buildings, communities) consume energy with their own appliances, which may have adjustable usage schedules over a day, a month, a season or even a year. One of the major objectives of the smart grid is to flatten the demand load of numerous agents (viz. consumers), such that the peak load can be avoided and power supply can feed the demand load at anytime on the grid. To this end, we propose two Energy Consumption Scheduling (ECS) problems for the appliances held by different agents at the demand side to effectively facilitate load leveling. Specifically, we mathematically model the ECS problems as Mixed-Integer Programming (MIP) problems using the data collected from different agents (e.g., their appliances’ energy consumption in every time slot and the total number of required in-use time slots, specific preferences of the in-use time slots for their appliances). Furthermore, we propose a novel algorithm to efficie... [more]
A States of Matter Search-Based Approach for Solving the Problem of Intelligent Power Allocation in Plug-in Hybrid Electric Vehicles
Arturo Valdivia-Gonzalez, Daniel Zaldívar, Fernando Fausto, Octavio Camarena, Erik Cuevas, Marco Perez-Cisneros
July 26, 2019 (v1)
Keywords: gravitational search (GSA), intelligent management, nature-inspired, particle swarm optimization (PSO), Plug-in Hybrid Electric Vehicles (PHEV), smart grid, state of matter search (SMS)
Recently, many researchers have proved that the electrification of the transport sector is a key for reducing both the emissions of green-house pollutants and the dependence on oil for transportation. As a result, Plug-in Hybrid Electric Vehicles (or PHEVs) are receiving never before seen increased attention. Consequently, large-scale penetration of PHEVs into the market is expected to take place in the near future, however, an unattended increase in the PHEVs needs may cause several technical problems which could potentially compromise the stability of power systems. As a result of the growing necessity for addressing such issues, topics related to the optimization of PHEVs’ charging infrastructures have captured the attention of many researchers. Related to this, several state-of-the-art swarm optimization methods (such as the well-known Particle Swarm Optimization (PSO) or the recently proposed Gravitational Search Algorithm (GSA) approach) have been successfully applied in the opti... [more]
Day-Ahead Scheduling Considering Demand Response as a Frequency Control Resource
Yu-Qing Bao, Yang Li, Beibei Wang, Minqiang Hu, Yanmin Zhou
July 26, 2019 (v1)
Keywords: day-ahead scheduling, demand response (DR), frequency control, unit commitment
The development of advanced metering technologies makes demand response (DR) able to provide fast response services, e.g., primary frequency control. It is recognized that DR can contribute to the primary frequency control like thermal generators. This paper proposes a day-ahead scheduling method that considers DR as a frequency control resource, so that the DR resources can be dispatched properly with other resources. In the proposed method, the objective of frequency control is realized by defining a frequency limit equation under a supposed contingency. The frequency response model is used to model the dynamics of system frequency. The nonlinear frequency limit equation is transformed to a linear arithmetic equation by piecewise linearization, so that the problem can be solved by mixed integer linear programming (MILP). Finally, the proposed method is verified on numerical examples.
Energy Rebound as a Potential Threat to a Low-Carbon Future: Findings from a New Exergy-Based National-Level Rebound Approach
Paul E. Brockway, Harry Saunders, Matthew K. Heun, Timothy J. Foxon, Julia K. Steinberger, John R. Barrett, Steve Sorrell
July 26, 2019 (v1)
Keywords: aggregate production function (APF), constant elasticity of substitution (CES) function, Energy Efficiency, energy policy, energy rebound, Exergy, Exergy Efficiency, macroeconomic rebound
150 years ago, Stanley Jevons introduced the concept of energy rebound: that anticipated energy efficiency savings may be “taken back” by behavioural responses. This is an important issue today because, if energy rebound is significant, this would hamper the effectiveness of energy efficiency policies aimed at reducing energy use and associated carbon emissions. However, empirical studies which estimate national energy rebound are rare and, perhaps as a result, rebound is largely ignored in energy-economy models and associated policy. A significant difficulty lies in the components of energy rebound assessed in empirical studies: most examine direct and indirect rebound in the static economy, excluding potentially significant rebound of the longer term structural response of the national economy. In response, we develop a novel exergy-based approach to estimate national energy rebound for the UK and US (1980⁻2010) and China (1981⁻2010). Exergy—as “available energy”—allows a consistent,... [more]
Hybrid Forecasting Approach Based on GRNN Neural Network and SVR Machine for Electricity Demand Forecasting
Weide Li, Xuan Yang, Hao Li, Lili Su
July 26, 2019 (v1)
Keywords: electricity demand forecasting, ensemble empirical mode decomposition (EEMD), generalized regression neural network (GRNN), support vector machine (SVM)
Accurate electric power demand forecasting plays a key role in electricity markets and power systems. The electric power demand is usually a non-linear problem due to various unknown reasons, which make it difficult to get accurate prediction by traditional methods. The purpose of this paper is to propose a novel hybrid forecasting method for managing and scheduling the electricity power. EEMD-SCGRNN-PSVR, the proposed new method, combines ensemble empirical mode decomposition (EEMD), seasonal adjustment (S), cross validation (C), general regression neural network (GRNN) and support vector regression machine optimized by the particle swarm optimization algorithm (PSVR). The main idea of EEMD-SCGRNN-PSVR is respectively to forecast waveform and trend component that hidden in demand series to substitute directly forecasting original electric demand. EEMD-SCGRNN-PSVR is used to predict the one week ahead half-hour’s electricity demand in two data sets (New South Wales (NSW) and Victorian... [more]
Multi-Time Scale Control of Demand Flexibility in Smart Distribution Networks
Bishnu P. Bhattarai, Kurt S. Myers, Birgitte Bak-Jensen, Sumit Paudyal
July 26, 2019 (v1)
Keywords: congestion management, demand response, electric vehicle, hierarchical control, microgrid, smart charging, smart grid
This paper presents a multi-timescale control strategy to deploy electric vehicle (EV) demand flexibility for simultaneously providing power balancing, grid congestion management, and economic benefits to participating actors. First, an EV charging problem is investigated from consumer, aggregator, and distribution system operator’s perspectives. A hierarchical control architecture (HCA) comprising scheduling, coordinative, and adaptive layers is then designed to realize their coordinative goal. This is realized by integrating multi-time scale controls that work from a day-ahead scheduling up to real-time adaptive control. The performance of the developed method is investigated with high EV penetration in a typical residential distribution grid. The simulation results demonstrate that HCA efficiently utilizes demand flexibility stemming from EVs to solve grid unbalancing and congestions with simultaneous maximization of economic benefits to the participating actors. This is ensured by... [more]
Designing Supply Networks in Automobile and Electronics Manufacturing Industries: A Multiplex Analysis
Myung Kyo Kim, Ram Narasimhan
July 25, 2019 (v1)
Keywords: allied engineering, network analysis, network multiplexity, supply chain management, supply network design
This study investigates the process of how the original equipment manufacturers (OEMs) in automobile and consumer electronics industries design their supply networks. In contrast to the sociological viewpoint, which regards the emergence of networks as a social and psychological phenomenon occurring among non-predetermined individuals, this paper attempts to provide a strategic supply network perspective that views the supply network as a strategic choice made by an OEM. Anchored in the multiplex investigation of supply network architectures, this study looks into the following specific questions: (1) Are an OEM’s strategic intent choices associated with supply network architecture and (2) If so, what differential effects do those strategic intents have on the architectural properties of the supply network? Further field investigations were conducted to provide deeper insights into the quantitative and qualitative findings from statistical analyses.
A Flexible Responsive Load Economic Model for Industrial Demands
Reza Sharifi, Amjad Anvari-Moghaddam, S. Hamid Fathi, Vahid Vahidinasab
July 25, 2019 (v1)
Keywords: consumer utility function, demand-side management, economic demand response model, electricity market restructuring
The best pricing method for any company in a perfectly competitive market is the pricing scheme with regards to the marginal cost. In contrast to this environment, there is a market with imperfect competition. In this market, the price can be affected by some players in the generation/demand side (i.e., suppliers and/or buyers). In the economic literature, “market power” refers to a company that has the power to affect prices. In fact, market power is often defined as the ability to divert prices from competitive levels. In the electricity market, especially because of the integration of intermittent renewable energy resources (RESs) along with the inflexibility of demand, there are levels of market power on the supply side. Hence, implementation of demand response (DR) programs is necessary to increase the flexibility of the demand side to deal with the intermittency of renewable generations and at the same time tackle the market power of the supply side. This paper uses economic theo... [more]
Realizing Energy Savings in Integrated Process Planning and Scheduling
Liangliang Jin, Chaoyong Zhang, Xinjiang Fei
July 5, 2019 (v1)
Keywords: carbon emission, energy saving, integrated process planning &, MILP models, multi-objective optimization, Scheduling, TOPSIS
The integration of scheduling and process planning can eliminate resource conflicts and hence improve the performance of a manufacturing system. However, the focus of most existing works is mainly on the optimization techniques to improve the makespan criterion instead of more efficient uses of energy. In fact, with a deteriorating global climate caused by massive coal-fired power consumption, carbon emission reduction in the manufacturing sector is becoming increasingly imperative. To ease the environmental burden caused by energy consumption, e.g., coal-fired power consumption in use of machine tools, this research considers both makespan as well as environmental performance criteria, e.g., total power consumption, in integrated process planning and scheduling using a novel multi-objective memetic algorithm to facilitate a potential amount of energy savings; this can be realized through a better use of resources with more efficient scheduling schemes. A mixed-integer linear programmi... [more]
Scheduling of Energy-Integrated Batch Process Systems Using a Pattern-Based Framework
Sujit Suresh Jogwar, Shrikant Mete, Channamallikarjun S. Mathpati
June 10, 2019 (v1)
Keywords: batch scheduling, energy integration, mixed-integer optimization, patterns
In this paper, a novel pattern-based method is developed for the generation of optimal schedules for energy-integrated batch process systems. The proposed methodology is based on the analysis of available schedules for the identification of repetitive patterns. It is shown that optimal schedules of energy-integrated batch processes are composed of several repeating sections (or building blocks), and their sizes and relative positions are dependent on the scheduling horizon and constraints. Based on such a decomposition, the proposed pattern-based algorithm generates an optimal schedule by computing the number and sequence of these blocks. The framework is then integrated with rigorous optimization-based approach wherein it is shown that the learning from the pattern-based solution significantly improves the performance of rigorous optimization. The main advantage of the pattern-based method is the significant reduction in computational time required to solve large scheduling problems,... [more]
Optimal Operating Schedule for Energy Storage System: Focusing on Efficient Energy Management for Microgrid
Sooyoung Jung, Yong Tae Yoon
June 8, 2019 (v1)
Keywords: demand response, energy management system, energy storage system, microgrid, optimal operating schedule, peak control, photovoltaic
A microgrid is a group of many small-scale distributed energy resources, such as solar/wind energy sources, diesel generators, energy storage units, and electric loads. As a small-scale power grid, it can be operated independently or within an existing power grid(s). The microgrid energy management system is a system that controls these components to achieve optimized operation in terms of price by reducing costs and maximizing efficiency in energy consumption. A post-Industry-4.0 consumer requires an optimal design and control of energy storage based on a demand forecast, using big data to stably supply clean, new, and renewable energy when necessary while maintaining a consistent level of quality. Thus, this study focused on software technology through which an optimized operation schedule for energy storage in a microgrid is derived. This energy storage operation schedule minimizes the costs involved in electricity use. For this, an optimization technique is used that sets an object... [more]
Robust Scheduling Optimization Model for Multi-Energy Interdependent System Based on Energy Storage Technology and Ground-Source Heat Pump
Zhongfu Tan, Hongwu Guo, Hongyu Lin, Qingkun Tan, Shenbo Yang, De Gejirifu, Liwei Ju, Xueying Song
April 15, 2019 (v1)
Keywords: distributed energy, Energy Storage, ground-source heat pump, PSO algorithm, robust scheduling optimization
The energy consumed by buildings makes up a significant part of total social energy consumption. The energy use rate of the traditional cooling and heating unit is low. A distributed cooling, heating, and power (CHP) system can achieve cascade use of energy and reduce the long-distance transportation of energy. Along with the wide use of ground-source heat pumps and energy storage technology, the combined cooling, heating, and power (CCHP) system coupled with a ground-source heat pump and energy storage technology is increasingly being used. Firstly, we proposed the construction of a CCHP system driven by distributed energy resources (DERs) including three subsystems of an electricity subsystem, a CCHP subsystem and an auxiliary heating subsystem as the object of study in this paper. Besides, with the goals of reducing carbon emissions, increasing energy efficiency, and minimizing system cost, a constraint mechanism based on the DOM-PSO (dynamic object method/particle swarm optimizatio... [more]
Dynamic Performance Assessment of Primary Frequency Modulation for a Power Control System Based on MATLAB
Shizhe Li, Yinsong Wang
April 9, 2019 (v1)
Keywords: dynamic performance assessment index, internal model control, least square estimation, power control system, primary frequency modulation, valve local flow coefficient
The primary frequency modulation (PFM) performance of a power control system (PCS) is an important factor affecting the security and stability of a power grid. The traditional control method is proportional integral (PI) control. In order to improve its dynamic control performance, a control method based on the combination of internal model control (IMC) and PI is proposed. Using the method of theoretical assessment and system identification, a simple simulated model of the typical PCS is established. According to the principle of system identification and the least square estimation (LSE) algorithm, the mathematical models of a generator and a built-in model are established. According to the four dynamic performance indexes, the main and auxiliary assessment index of the PCS are defined, and the benchmark and the result of the performance assessment are given. According to three different structures, the PFM dynamic performance of the PCS is analyzed separately. According to the dynam... [more]
Research on a Micro-Grid Frequency Modulation Strategy Based on Optimal Utilization of Air Conditioners
Qingzhu Wan, Yuan Bian, Yalan Chen
February 27, 2019 (v1)
Keywords: energy storage system, frequency control, micro-grid, thermostatically controlled loads
With the proportion of air conditioners increasing gradually, they can provide a certain amount of frequency-controlled reserves for a micro-grid. Optimizing utilization of air conditioners and considering load response characteristics and customer comfort, the frequency adjustment model is a quadratic function model between the trigger temperature of the air conditioner compressor, and frequency variation is provided, which can be used to regulate the trigger temperature of the air conditioner when the micro-grid frequency rises and falls. This frequency adjustment model combines a primary frequency modulation method and a secondary frequency modulation method of the energy storage system, in order to optimize the frequency of a micro-grid. The simulation results show that the frequency modulation strategy for air conditioners can effectively improve the frequency modulation ability of air conditioners and frequency modulation effects of a micro-grid in coordination with an energy sto... [more]
Evaluation of Conservation Voltage Reduction with Analytic Hierarchy Process: A Decision Support Framework in Grid Operations Planning
Kyungsung An, Hao Jan Liu, Hao Zhu, Zhao Yang Dong, Kyeon Hur
February 27, 2019 (v1)
Keywords: analytic hierarchy process (AHP), conservation voltage reduction (CVR), CVR factor, integer programming, power transfer distribution factor (PTDF), voltage sensitivity factor (VSF)
This paper presents a systematic framework to evaluate the performance of conservation voltage reduction (CVR) by determining suitable substations for CVR in operations planning. Existing CVR planning practice generally only focuses on the energy saving aspect without taking other underlying attributes into account, i.e., network topology and reduced voltage effects on other substations. To secure the desired operating reserve and avoid any adverse impacts, these attributes should be considered for implementing CVR more effectively. This research develops a practical decision-making framework based on the analytic hierarchy process (AHP) to quantify several of the aforementioned attributes. Candidate substations for CVR deployment are prioritized such that performances are compared in terms of power transfer distribution factor (PTDF), voltage sensitivity factor (VSF), and CVR factor. In addition, to meet a specified reserve requirement, an integer programming approach is adopted to se... [more]
Green Small Cell Operation of Ultra-Dense Networks Using Device Assistance
Gilsoo Lee, Hongseok Kim
February 27, 2019 (v1)
Keywords: belief propagation, cellular networks, Energy Efficiency, Optimization, small cell
As higher performance is demanded in 5G networks, energy consumption in wireless networks increases along with the advances of various technologies, so enhancing energy efficiency also becomes an important goal to implement 5G wireless networks. In this paper, we study the energy efficiency maximization problem focused on finding a suitable set of turned-on small cell access points (APs). Finding the suitable on/off states of APs is challenging since the APs can be deployed by users while centralized network planning is not always possible. Therefore, when APs in small cells are randomly deployed and thus redundant in many cases, a mechanism of dynamic AP turning-on/off is required. We propose a device-assisted framework that exploits feedback messages from the user equipment (UE). To solve the problem, we apply an optimization method using belief propagation (BP) on a factor graph. Then, we propose a family of online algorithms inspired by BP, called DANCE, that requires low computati... [more]
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