Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Forecasting and control for wind power systems
Wind energy has become the world's fastest growing source of clean and renewable energy andnow contributes a large proportion of total power generation. This proportion will continue toincrease because of the global preference for a clean and renewable energy source. However,wind power is difficult to integrate into traditional generation and distribution systems with currenttechnology because it is intermittent, unpredictable and volatile. Thus, it is difficult to matchwind generation to energy demand, and the imbalances between demand and generation can causeadverse voltage variations. This power quality problem cannot be solved effectively only by renewablegenerating technology and/or power electronics. The adverse effects of wind generatorson power quality are currently an important issue. As a whole, wind power integration challengethe power quality, energy planning and power flow controls in the grid. This can be more severein weak networks, where the whole wind power source may even be disconnected from the gridas an extreme case.In this thesis, we apply wind power prediction, battery energy storage and concepts and ideasfrom Model Predictive Control (MPC) theory to make wind power more attractive and reliable forpower utility companies. The research consists of the following steps.We have developed a wind power prediction model that allows accurate prediction 10 minutesahead. This is combined with a direction-dependent power curve model, which provides significantimprovement in the accuracy of predictions. We have developed a wind power smoothing model, based on a Battery Energy Storage System (BESS), the above prediction model and MPC,to suppress the output power fluctuations of a wind farm, thus allowing smoother operation of thepower grid and improved power quality.The concept was further developed to include power frequency regulation, and to optimiseBESS capacity. This model supported excellent control of power quality with a reduced BESSrequirement by distributing the BESS capacity efficiently. Our control system also ensures morecareful regulation of BESS charging/discharging rates to allow longer battery life. It thus reducesthe capital cost of the BESS system and extends its operational life, reducing the total capital costof wind power generation.While our models were developed for wind power and battery storage, they could be appliedequally effectively to other generation and storage technologies.
Forecasting and control for wind power systems
Wind energy has become the world's fastest growing source of clean and renewable energy andnow contributes a large proportion of total power generation. This proportion will continue toincrease because of the global preference for a clean and renewable energy source. However,wind power is difficult to integrate into traditional generation and distribution systems with currenttechnology because it is intermittent, unpredictable and volatile. Thus, it is difficult to matchwind generation to energy demand, and the imbalances between demand and generation can causeadverse voltage variations. This power quality problem cannot be solved effectively only by renewablegenerating technology and/or power electronics. The adverse effects of wind generatorson power quality are currently an important issue. As a whole, wind power integration challengethe power quality, energy planning and power flow controls in the grid. This can be more severein weak networks, where the whole wind power source may even be disconnected from the gridas an extreme case.In this thesis, we apply wind power prediction, battery energy storage and concepts and ideasfrom Model Predictive Control (MPC) theory to make wind power more attractive and reliable forpower utility companies. The research consists of the following steps.We have developed a wind power prediction model that allows accurate prediction 10 minutesahead. This is combined with a direction-dependent power curve model, which provides significantimprovement in the accuracy of predictions. We have developed a wind power smoothing model, based on a Battery Energy Storage System (BESS), the above prediction model and MPC,to suppress the output power fluctuations of a wind farm, thus allowing smoother operation of thepower grid and improved power quality.The concept was further developed to include power frequency regulation, and to optimiseBESS capacity. This model supported excellent control of power quality with a reduced BESSrequirement by distributing the BESS capacity efficiently. Our control system also ensures morecareful regulation of BESS charging/discharging rates to allow longer battery life. It thus reducesthe capital cost of the BESS system and extends its operational life, reducing the total capital costof wind power generation.While our models were developed for wind power and battery storage, they could be appliedequally effectively to other generation and storage technologies.
Forecasting and control for wind power systems
Khalid, Muhammad (Autor:in)
2011
Hochschulschrift
Elektronische Ressource
Englisch
Wind power forecasting error-based dispatch method for wind farm cluster
| DOAJ | 2013