The building sector and its heating and cooling represents one of the major consumer of energy worldwide. Simultaneously, the share of fluctuating generation of renewable energies in the energy mix increases. Therefor...The building sector and its heating and cooling represents one of the major consumer of energy worldwide. Simultaneously, the share of fluctuating generation of renewable energies in the energy mix increases. Therefore storage and demand side management technologies are required. The new adaptive and predictive control algorithm for thermally activated building systems （TABS） based on multiple linear regression （AMLR） presented in this paper enables the application of demand side management （DSM） strategies. Based on simulations, different strategies have been compared with each other. By applying the AMLR algorithm, electricity energy cost savings of 38% could be achieved compared to the conventional control strategy for TABS, while increasing the thermal comfort. At the same time, thermal energy demand can be reduced in the range between 4% to 8%, and pump operation time from 86% to 89%.展开更多
The violently penetration of renewables in power supply network leads to situations, by which the offer exceeds the demand. Therefore, it is necessary to include a system for processes＇ management. SmartGrid is a pla...The violently penetration of renewables in power supply network leads to situations, by which the offer exceeds the demand. Therefore, it is necessary to include a system for processes＇ management. SmartGrid is a platform over the power supply network. It is represented with its network and services, which also have to be managed. The paper aims to show the second stage of SmartGrid management modeling. It meets heterogeneous requirements of the actors--subscriber without photovoltaics, subscriber with photovoltaics, service provider, network operator, and network elements operator--to service and network management and is oriented to functional areas, covering the life cycle of power supply service： Design, Planning, Installation, Provision, Configuration, Maintenance, Performance, Accounting, Buying Up, Security and Subscriber Control. Functional models for existing networks--telecommunications--are used and they are adapted to power supply. The results are illustrated with three functional areas for service management： Accounting, Buying Up, and Performance. The synthesis of network management functions and network element management functions are similar.展开更多
In this paper,we present a Q-Learning optimization algorithm for smart home HVAC systems.The proposed algorithm combines new convex deep neural network models with model predictive control(MPC)techniques.More specific...In this paper,we present a Q-Learning optimization algorithm for smart home HVAC systems.The proposed algorithm combines new convex deep neural network models with model predictive control(MPC)techniques.More specifically,new input convex long short-term memory(ICLSTM)models are employed to predict dynamic states in an MPC optimal control technique integrated within a Q-Learning reinforcement learning(RL)algorithm to further improve the learned temporal behaviors of nonlinear HVAC systems.As a novel RL approach,the proposed algorithm generates day-ahead HVAC demand response(DR)signals in smart homes that optimally reduce and/or shift peak energy usage,reduce electricity costs,minimize user discomfort,and honor in a best-effort way the recommendations from utility/aggregator,which in turn has impact on the overall well being of the distribution network controlled by the aggregator.The proposed Q-Learning optimization algorithm,based on epsilon-model predictive control(e-MPC),can be implemented as a control agent that is executed by the smart house energy management(SHEM)system that we assume exists in the smart home,which can interact with the energy provider of the distribution network,i.e.,utility/aggregator,via the smart meter.The output generated by the proposed control agent represents day-ahead local DR signals in the form of temperature setpoints for the HVAC system that are found by the optimization process to lead to desired trade-offs between electricity cost and user discomfort.The proposed algorithm can be used in smart homes with passive HVAC controllers,which solely react to end-user setpoints,to transform them into smart homes with active HVAC controllers.Such systems not only respond to the preferences of the end-user but also incorporate an external control signal provided by the utility or aggregator.Simulation experiments conducted with a custom simulation tool demonstrate that the proposed optimization framework can offer significant benefits.It achieves 87%higher success rate in optimizing setpoints in the desired range,thereby resulting in up to 15%energy savings and zero temperature discomfort.展开更多
In this paper the provision of flexible generation is investigated by extracting steam from Rankine-cycle power stations during off-peak demand in order to charge thermal tanks that contain suitable phase-change mater...In this paper the provision of flexible generation is investigated by extracting steam from Rankine-cycle power stations during off-peak demand in order to charge thermal tanks that contain suitable phase-change materials(PCMs);at a later time when this is required and/or is economically effective,these thermal energy storage(TES)tanks can act as the heat sources of secondary thermal power plants in order to generate power,for example as evaporators of,e.g.,organic Rankine cycle(ORC)plants that are suitable for power generation at reduced temperatures and smaller scales.This type of solution offers greater flexibility than TES-only technologies that store thermal energy and release it back to the base power station,since it allows both derating but also over-generation compared to the base power-station capacity.The solution is applied in a case study of a 50-MW rated oil-fired power station unit at the autonomous system of Crete.The optimal operation of the TES system is investigated,by solving a modified Unit Commitment-Economic Dispatch optimization problem,which includes the TES operating constraints.The results indicate that for most of the scenarios the discounted payback period is lower than 12 years,while in few cases the payback period is 5 years.展开更多
With the latest introduction of the demand side management （DSM） in smart grids, the power distribution units are able to modify the load schedules of the consumers. This involves a co-operative interaction of the u...With the latest introduction of the demand side management （DSM） in smart grids, the power distribution units are able to modify the load schedules of the consumers. This involves a co-operative interaction of the utility and the consumers so as to achieve customer load modifications in which the customer, utility and society all are benefited. The interaction is performed with the help of the devices known as the smart meter. This paper shows the use of game theory and logical mathematical expressions in order to achieve the objec- tives. The objectives are to minimize the peak to average ratio （PAR） and the energy cost. The outcome of the game between supplier and customers helps to shape the load profile. The design proposed in this paper is very user- friendly and is based on simple logarithmic programming computations. In this paper, residential, commercial and industrial types of loads are taken into account. A basic 24 h load schedule along with the fluctuating prices at each hour of the day is forecasted by the supplier of the various shiftable and non-shiftable loads and then that schedule is conveyed to the user. The users are encouraged to shift their high load devices to off-peak hours which will not only reduce their electricity costs but also substantially reduce the PAR in the load demand.展开更多
基金supported by the Ministry of Science,Research and Arts(MWK)of Baden-Württemberg,Germany,as part of a Ph.D.scholarship
文摘The building sector and its heating and cooling represents one of the major consumer of energy worldwide. Simultaneously, the share of fluctuating generation of renewable energies in the energy mix increases. Therefore storage and demand side management technologies are required. The new adaptive and predictive control algorithm for thermally activated building systems （TABS） based on multiple linear regression （AMLR） presented in this paper enables the application of demand side management （DSM） strategies. Based on simulations, different strategies have been compared with each other. By applying the AMLR algorithm, electricity energy cost savings of 38% could be achieved compared to the conventional control strategy for TABS, while increasing the thermal comfort. At the same time, thermal energy demand can be reduced in the range between 4% to 8%, and pump operation time from 86% to 89%.
文摘The violently penetration of renewables in power supply network leads to situations, by which the offer exceeds the demand. Therefore, it is necessary to include a system for processes＇ management. SmartGrid is a platform over the power supply network. It is represented with its network and services, which also have to be managed. The paper aims to show the second stage of SmartGrid management modeling. It meets heterogeneous requirements of the actors--subscriber without photovoltaics, subscriber with photovoltaics, service provider, network operator, and network elements operator--to service and network management and is oriented to functional areas, covering the life cycle of power supply service： Design, Planning, Installation, Provision, Configuration, Maintenance, Performance, Accounting, Buying Up, Security and Subscriber Control. Functional models for existing networks--telecommunications--are used and they are adapted to power supply. The results are illustrated with three functional areas for service management： Accounting, Buying Up, and Performance. The synthesis of network management functions and network element management functions are similar.
基金supported by an award from the National Science Foundation,USA grant ECCF 1936494..
文摘In this paper,we present a Q-Learning optimization algorithm for smart home HVAC systems.The proposed algorithm combines new convex deep neural network models with model predictive control(MPC)techniques.More specifically,new input convex long short-term memory(ICLSTM)models are employed to predict dynamic states in an MPC optimal control technique integrated within a Q-Learning reinforcement learning(RL)algorithm to further improve the learned temporal behaviors of nonlinear HVAC systems.As a novel RL approach,the proposed algorithm generates day-ahead HVAC demand response(DR)signals in smart homes that optimally reduce and/or shift peak energy usage,reduce electricity costs,minimize user discomfort,and honor in a best-effort way the recommendations from utility/aggregator,which in turn has impact on the overall well being of the distribution network controlled by the aggregator.The proposed Q-Learning optimization algorithm,based on epsilon-model predictive control(e-MPC),can be implemented as a control agent that is executed by the smart house energy management(SHEM)system that we assume exists in the smart home,which can interact with the energy provider of the distribution network,i.e.,utility/aggregator,via the smart meter.The output generated by the proposed control agent represents day-ahead local DR signals in the form of temperature setpoints for the HVAC system that are found by the optimization process to lead to desired trade-offs between electricity cost and user discomfort.The proposed algorithm can be used in smart homes with passive HVAC controllers,which solely react to end-user setpoints,to transform them into smart homes with active HVAC controllers.Such systems not only respond to the preferences of the end-user but also incorporate an external control signal provided by the utility or aggregator.Simulation experiments conducted with a custom simulation tool demonstrate that the proposed optimization framework can offer significant benefits.It achieves 87%higher success rate in optimizing setpoints in the desired range,thereby resulting in up to 15%energy savings and zero temperature discomfort.
文摘In this paper the provision of flexible generation is investigated by extracting steam from Rankine-cycle power stations during off-peak demand in order to charge thermal tanks that contain suitable phase-change materials(PCMs);at a later time when this is required and/or is economically effective,these thermal energy storage(TES)tanks can act as the heat sources of secondary thermal power plants in order to generate power,for example as evaporators of,e.g.,organic Rankine cycle(ORC)plants that are suitable for power generation at reduced temperatures and smaller scales.This type of solution offers greater flexibility than TES-only technologies that store thermal energy and release it back to the base power station,since it allows both derating but also over-generation compared to the base power-station capacity.The solution is applied in a case study of a 50-MW rated oil-fired power station unit at the autonomous system of Crete.The optimal operation of the TES system is investigated,by solving a modified Unit Commitment-Economic Dispatch optimization problem,which includes the TES operating constraints.The results indicate that for most of the scenarios the discounted payback period is lower than 12 years,while in few cases the payback period is 5 years.
文摘With the latest introduction of the demand side management （DSM） in smart grids, the power distribution units are able to modify the load schedules of the consumers. This involves a co-operative interaction of the utility and the consumers so as to achieve customer load modifications in which the customer, utility and society all are benefited. The interaction is performed with the help of the devices known as the smart meter. This paper shows the use of game theory and logical mathematical expressions in order to achieve the objec- tives. The objectives are to minimize the peak to average ratio （PAR） and the energy cost. The outcome of the game between supplier and customers helps to shape the load profile. The design proposed in this paper is very user- friendly and is based on simple logarithmic programming computations. In this paper, residential, commercial and industrial types of loads are taken into account. A basic 24 h load schedule along with the fluctuating prices at each hour of the day is forecasted by the supplier of the various shiftable and non-shiftable loads and then that schedule is conveyed to the user. The users are encouraged to shift their high load devices to off-peak hours which will not only reduce their electricity costs but also substantially reduce the PAR in the load demand.
