2014 IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG)最新文献_第3页

An evolutionary approach for the demand side management optimization in smart grid 智能电网需求侧管理优化的演化方法

2014 IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG) Pub Date : 2014-12-01 DOI: 10.1109/CIASG.2014.7011561

A. R. F. Vidal, L. Jacobs, L. Batista

{"title":"An evolutionary approach for the demand side management optimization in smart grid","authors":"A. R. F. Vidal, L. Jacobs, L. Batista","doi":"10.1109/CIASG.2014.7011561","DOIUrl":"https://doi.org/10.1109/CIASG.2014.7011561","url":null,"abstract":"An important function of a Smart Grid (SG) is the Demand Side Management (DSM), which consists on controlling loads at customers side, aiming to operate the system with major efficiency and sustainability. The main advantages of this technique are (i) the decrease of demand curve's peak, that results on smoother load profile and (ii) the reduction of both operational costs and the requirement of new investments in the system. The customer can save money by using loads on schedules with lower taxes instead of schedules with higher taxes. In this context, this work proposes a simple metaheuristic to solve the problem of DSM on smart grid. The suggested approach is based on the concept of day-ahead load shifting, which implies on the exchange of the use schedules planned for the next day and aims to obtain the lowest possible cost of energy. The demand management is modeled as an optimization problem whose solution is obtained by using an Evolutionary Algorithm (EA). The experimental tests are carried out considering a smart grid with three distinct demand areas, the first with residential clients, other one with commercial clients and a third one with industrial clients, all of them possessing a major number of controllable loads of diverse types. The obtained results were significant in all three areas, pointing substantial cost reductions for the customers, mainly on the industrial area.","PeriodicalId":166543,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132301855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 28

Accurate localized short term weather prediction for renewables planning 为可再生能源规划提供准确的局部短期天气预报

2014 IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG) Pub Date : 2014-12-01 DOI: 10.1109/CIASG.2014.7011547

D. Corne, M. Dissanayake, A. Peacock, S. Galloway, Eddie Owens

{"title":"Accurate localized short term weather prediction for renewables planning","authors":"D. Corne, M. Dissanayake, A. Peacock, S. Galloway, Eddie Owens","doi":"10.1109/CIASG.2014.7011547","DOIUrl":"https://doi.org/10.1109/CIASG.2014.7011547","url":null,"abstract":"Short-term prediction of meteorological variables is important for many applications. For example, many `smart grid' planning and control scenarios rely on accurate short term prediction of renewable energy generation, which in turn requires accurate forecasts of wind-speed, cloud-cover, and other such variables. Accurate short-term weather forecasting therefore enables smooth integration of renewables into future intelligent power systems. Weather forecasting at a specific location is currently achieved by numerical weather prediction (NWP), or by statistical models built from local time series data, or by a hybrid of these two methods broadly known as `downscaling'. We introduce a new data-intensive approach to localized short-term weather prediction that relies on harvesting multiple freely available observations and forecasts pertaining to the wider geographic region. Our hypothesis is that NWP-based forecast resources, despite the benefit of a dynamical physics-based model, tend to be only sparsely informed by observation-based inputs at a local level, while statistical downscaling models, though locally well-informed, invariably miss the opportunity to include rich additional data sources concerning the wider local region. By harvesting the data stream of multiple forecasts and observations from the wider local region we expect to achieve better accuracy than available otherwise. We describe the approach and demonstrate results for three locations, focusing on the 1hr-24hrs ahead forecasting of variables crucial for renewables forecasting. This work is part of the ORIGIN EU FP7 project (www.origin-concept.eu) and the weather forecasting approach, used in ORIGIN as input for both demand and renewables prediction, began live operation (initially for three European locations) in October 2014.","PeriodicalId":166543,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131126154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

Comparison of echo state network and extreme learning machine for PV power prediction 回声状态网络与极限学习机在光伏发电功率预测中的比较

2014 IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG) Pub Date : 2014-12-01 DOI: 10.1109/CIASG.2014.7011546

Iroshani Jayawardene, G. Venayagamoorthy

引用次数: 16

Multi-machine power system control based on dual heuristic dynamic programming 基于对偶启发式动态规划的多机电力系统控制

2014 IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG) Pub Date : 2014-12-01 DOI: 10.1109/CIASG.2014.7011566

Zhen Ni, Yufei Tang, Haibo He, J. Wen

引用次数: 23

Pulsed power network based on decentralized intelligence for reliable and low loss electrical power distribution 基于分散智能的脉冲电网实现可靠、低损耗的电力分配

2014 IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG) Pub Date : 2014-12-01 DOI: 10.1109/CIASG.2014.7011558

Hisayoshi Sugiyama

{"title":"Pulsed power network based on decentralized intelligence for reliable and low loss electrical power distribution","authors":"Hisayoshi Sugiyama","doi":"10.1109/CIASG.2014.7011558","DOIUrl":"https://doi.org/10.1109/CIASG.2014.7011558","url":null,"abstract":"Pulsed power network is proposed for reliable and low loss electrical power distribution among various type of power sources and consumers. The proposed scheme is a derivative of power packet network so far investigated that has affinity with dispersion type power sources and has manageability of energy coloring in the process of power distribution. In addition to these advantages, the proposed scheme has system reliability and low loss property because of its intelligent operation performed by individual nodes and direct relaying by power routers. In the proposed scheme, power transmission is decomposed into a series of pulses placed at specified power slots in continuous time frames that are synchronized over the network. The power slots are pre-reserved based on information exchanges among neighboring nodes following inherent protocol of the proposed scheme. Because of this power slot reservation based on decentralized intelligence, power pulses are directly transmitted from various power sources to consumers with low power dissipation even though a partial failure occurs in the network. The network performance of the proposed scheme is simulated to confirm the protocol for the power slot reservation.","PeriodicalId":166543,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122645972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Smart grid energy fraud detection using artificial neural networks 基于人工神经网络的智能电网能源欺诈检测

2014 IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG) Pub Date : 2014-12-01 DOI: 10.1109/CIASG.2014.7011557

Vitaly Ford, Ambareen Siraj, W. Eberle

引用次数: 96

A uniform implementation scheme for evolutionary optimization algorithms and the experimental implementation of an ACO based MPPT for PV systems under partial shading 一种进化优化算法的统一实现方案及基于蚁群算法的部分遮阳光伏系统MPPT的实验实现

2014 IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG) Pub Date : 2014-12-01 DOI: 10.1109/CIASG.2014.7011560

L. Jiang, D. Maskell

{"title":"A uniform implementation scheme for evolutionary optimization algorithms and the experimental implementation of an ACO based MPPT for PV systems under partial shading","authors":"L. Jiang, D. Maskell","doi":"10.1109/CIASG.2014.7011560","DOIUrl":"https://doi.org/10.1109/CIASG.2014.7011560","url":null,"abstract":"Partial shading is one of the important issues in maximum power point (MPP) tracking (MPPT) for photovoltaic (PV) systems. Multiple peaks on the power-voltage (P-V) curve under partial shading conditions can result in a conventional MPPT technique failing to track the global MPP, thus causing large power losses. Whereas, evolutionary optimization algorithms exhibit many advantages when applying them to MPPT, such as, the ability to track the global MPP, no requirement for irradiance or temperature sensors, system independence without knowledge of the PV system in advance, reduced current/voltage sensors compared to conventional methods when applied to PV systems with a distributed MPPT structure. This paper presents a uniform scheme for implementing evolutionary algorithms into the MPPT under various PV array structures. The effectiveness of the proposed method is verified both by simulations and experimental setup. The implementation of the ant colony optimization (ACO) based MPPT is conducted using this uniform scheme. In addition, a strategy to accelerate the convergence speed, which is important in systems with partial shading caused by rapid irradiance change, is also discussed.","PeriodicalId":166543,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132209960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 30

A time series ensemble method to predict wind power 风电功率预测的时间序列集合方法

2014 IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG) Pub Date : 1900-01-01 DOI: 10.1109/CIASG.2014.7011544

Sumaira Tasnim, Ashfaqur Rahman, Gm. Shafiullah, A. Oo, A. Stojcevski

引用次数: 7