A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Optimal management of microgrids
Desenvolupament dels models matemàtics necessaris per a controlar de forma òptima la microxarxa existent als laboratoris del Institut de Recerca en Energia de Catalunya. Els algoritmes s'implementaran per tal de simular el comportament i posteriorment es programaran directament sobre els elements de la microxarxa per verificar el seu correcte funcionament. Desenvolupament dels models matemàtics necessaris per a controlar de forma òptima la microxarxa existent als laboratoris del Institut de Recerca en Energia de Catalunya. Els algoritmes s'implementaran per tal de simular el comportament i posteriorment es programaran directament sobre els elements de la microxarxa per verificar el seu correcte funcionament ; Current mankind is facing a global dilemma with energy demand increasing, while di- minishing traditional energy resources. Increase energy e ciency and sustainability are becoming more necessary. In this framework, smart grids and microgrids are the key in the near future where a decentralization of energy generation is expected. An advantage of these type of grids is that balancing between energy generation, storage, and consump- tion can be realized most e ciently the closer the physical location of generation, storage and demand is the controller. This reduces the need for centralized communication, en- ables autonomous operations of increasingly smaller sections of the distribution grid and decreases the losses by distant distribution. Within this framework and from the point of view of microgrid energy management, economic scheduling for generation devices, storage systems and loads is a crucial problem. Performance an optimization process is necessary to minimize the operating costs while several operational constraints are taken into account. Energy management is carried out by MCC (Microgrid Central Controller) in three steps: tertiary, secondary and primary controls. The rst management step is executed one day-ahead and has two objectives. The rst is economic optimization using a program based on an Economic Dispatch and an Unit Commitment problem. The second objective is to improve the pro tability of the supply and demand balance by interacting with the grid and taking advantage of the V2G (vehicletogrid) capability of the charging spot, and to generate a schedule over all components of the microgrid. The rest of the controls are executed on day of operation in order to adjust the output power levels. The secondary control receives the scheduling plan created by tertiary control and taking into account current data, corrects the power outputs of generation units.Exchanged power with the grid and storage states of charge programmed by the tertiary control are ensured.Finally, the primary control regulates the energy ow in real time and ensures a proper operation to address any unexpected issues although, this control is not considered in the project.
Optimal management of microgrids
Desenvolupament dels models matemàtics necessaris per a controlar de forma òptima la microxarxa existent als laboratoris del Institut de Recerca en Energia de Catalunya. Els algoritmes s'implementaran per tal de simular el comportament i posteriorment es programaran directament sobre els elements de la microxarxa per verificar el seu correcte funcionament. Desenvolupament dels models matemàtics necessaris per a controlar de forma òptima la microxarxa existent als laboratoris del Institut de Recerca en Energia de Catalunya. Els algoritmes s'implementaran per tal de simular el comportament i posteriorment es programaran directament sobre els elements de la microxarxa per verificar el seu correcte funcionament ; Current mankind is facing a global dilemma with energy demand increasing, while di- minishing traditional energy resources. Increase energy e ciency and sustainability are becoming more necessary. In this framework, smart grids and microgrids are the key in the near future where a decentralization of energy generation is expected. An advantage of these type of grids is that balancing between energy generation, storage, and consump- tion can be realized most e ciently the closer the physical location of generation, storage and demand is the controller. This reduces the need for centralized communication, en- ables autonomous operations of increasingly smaller sections of the distribution grid and decreases the losses by distant distribution. Within this framework and from the point of view of microgrid energy management, economic scheduling for generation devices, storage systems and loads is a crucial problem. Performance an optimization process is necessary to minimize the operating costs while several operational constraints are taken into account. Energy management is carried out by MCC (Microgrid Central Controller) in three steps: tertiary, secondary and primary controls. The rst management step is executed one day-ahead and has two objectives. The rst is economic optimization using a program based on an Economic Dispatch and an Unit Commitment problem. The second objective is to improve the pro tability of the supply and demand balance by interacting with the grid and taking advantage of the V2G (vehicletogrid) capability of the charging spot, and to generate a schedule over all components of the microgrid. The rest of the controls are executed on day of operation in order to adjust the output power levels. The secondary control receives the scheduling plan created by tertiary control and taking into account current data, corrects the power outputs of generation units.Exchanged power with the grid and storage states of charge programmed by the tertiary control are ensured.Finally, the primary control regulates the energy ow in real time and ensures a proper operation to address any unexpected issues although, this control is not considered in the project.
Optimal management of microgrids
2012-07-01
Theses
Electronic Resource
English
microgrid , energy system optimization , electric vehicle , Investigació operativa , mathematical programming::90B Operations research and management science , Administració--Models matemàtics , Classificació AMS::90 Operations research , Management science , smart grids , Àrees temàtiques de la UPC::Matemàtiques i estadística::Investigació operativa::Optimització , Operations research
DDC:
690
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