• Energy Research

Abstract Electric vehicles (EVs) are widely regarded as valuable assets in the smart grid as distributed energy resources in addition to their primary transportation function. However, connecting EVs to the distribution network and recharging the EV batteries without any control may overload the transformers and cables during peak hours when the penetration of EVs is relatively high. In this study, a two level hierarchical control method for integrating EVs into the distribution network is proposed to coordinate the self-interests and operational constraints of two actors, the EV owner and Distribution system operator (DSO), facilitated by the introduction of the fleet operator (FO) and the grid capacity market operator (CMO). Unlike the typical hierarchical control system where the upper level controller commands the low level unit to execute the actions, in this study, market based control are applied both in the upper and low level of the hierarchical system. Specifically, in the upper level of the hierarchy, distribution system operator uses market based control to coordinate the fleet operator׳s power schedule. In the low level of the hierarchy, the fleet operator use market based control to allocate the charging power to the individual EVs, by using market based control, the proposed method considers the flexibility of EVs through the presence of the response-weighting factor to the shadow price sent out by the FO. Furthermore, to fully demonstrate the coordination behavior of the proposed control strategy, we built a multi-agent system (MAS) that is based on the co-simulation environment of JACK, Matlab and Simulink. A use case of the MAS and the results of running the system are presented to intuitively illustrate the effectiveness of the proposed solutions.

Abstract Electric vehicles (EVs) are widely regarded as valuable assets in the smart grid as distributed energy resources in addition to their primary transportation function. However, connecting EVs to the distribution network and recharging the EV batteries without any control may overload the transformers and cables during peak hours when the penetration of EVs is relatively high. In this study, a two level hierarchical control method for integrating EVs into the distribution network is proposed to coordinate the self-interests and operational constraints of two actors, the EV owner and Distribution system operator (DSO), facilitated by the introduction of the fleet operator (FO) and the grid capacity market operator (CMO). Unlike the typical hierarchical control system where the upper level controller commands the low level unit to execute the actions, in this study, market based control are applied both in the upper and low level of the hierarchical system. Specifically, in the upper level of the hierarchy, distribution system operator uses market based control to coordinate the fleet operator׳s power schedule. In the low level of the hierarchy, the fleet operator use market based control to allocate the charging power to the individual EVs, by using market based control, the proposed method considers the flexibility of EVs through the presence of the response-weighting factor to the shadow price sent out by the FO. Furthermore, to fully demonstrate the coordination behavior of the proposed control strategy, we built a multi-agent system (MAS) that is based on the co-simulation environment of JACK, Matlab and Simulink. A use case of the MAS and the results of running the system are presented to intuitively illustrate the effectiveness of the proposed solutions.

This paper presents a mechanism for developing knowledge based support for real time application of multiagent systems (MAS) in control, automation and diagnosis of electric power systems. In particular it presents a way for autonomous agents to utilize a qualitative means-ends based model for reasoning about control situations. The proposed mechanism has been used in different scenarios of electric power distribution system protection and control. Results show that agents can use local models of their environment and coordinate with other agents to analyze and understand a disturbance situation and choose an appropriate control action. The paper also elaborates on real time interfacing between multi-agent systems and industry standard distribution automation and control system.

This paper presents a mechanism for developing knowledge based support for real time application of multiagent systems (MAS) in control, automation and diagnosis of electric power systems. In particular it presents a way for autonomous agents to utilize a qualitative means-ends based model for reasoning about control situations. The proposed mechanism has been used in different scenarios of electric power distribution system protection and control. Results show that agents can use local models of their environment and coordinate with other agents to analyze and understand a disturbance situation and choose an appropriate control action. The paper also elaborates on real time interfacing between multi-agent systems and industry standard distribution automation and control system.

Abstract When severe accidents happen in nuclear power plants (NPPs), although there are candidate high-level actions (CHLAs) that can be selected from the severe accident management guidance (SAMG) to mitigate consequences, operators may still have cognitive challenges to identify available plant sources and then formulate executable action plans to implement the chosen actions. The difficulties faced by humans may be resolved by a decision support system taking advantages of computers’ knowledge representation and inferential capability. This article presents a systematic approach for synthesizing operating procedures that can achieve operational goals with currently available plant resources. Multilevel flow modeling (MFM), whose representation has been proved to be consistent with the general emergency procedure development, is used as knowledge basis for the action planning. A rule reasoning method is proposed to infer about actions from perspective of functions’ state transition. A model-based and rule-based operating procedure synthesis (OPS) system is developed with a rule engine. The OPS system is applied to elaborate existing high level strategies for dealing with station blackout (SBO) in a BWR plant as multiple executable action plans. The results shows some untypical ways of implementing available systems for achieving current safety goals, which may be considered in the further SAM. Moreover, what critical plant resources should be well prepared in advance is also indicated from the case study. Finally, limitation of MFM and the develop OPS system on planning SAM strategies are discussed.

Abstract When severe accidents happen in nuclear power plants (NPPs), although there are candidate high-level actions (CHLAs) that can be selected from the severe accident management guidance (SAMG) to mitigate consequences, operators may still have cognitive challenges to identify available plant sources and then formulate executable action plans to implement the chosen actions. The difficulties faced by humans may be resolved by a decision support system taking advantages of computers’ knowledge representation and inferential capability. This article presents a systematic approach for synthesizing operating procedures that can achieve operational goals with currently available plant resources. Multilevel flow modeling (MFM), whose representation has been proved to be consistent with the general emergency procedure development, is used as knowledge basis for the action planning. A rule reasoning method is proposed to infer about actions from perspective of functions’ state transition. A model-based and rule-based operating procedure synthesis (OPS) system is developed with a rule engine. The OPS system is applied to elaborate existing high level strategies for dealing with station blackout (SBO) in a BWR plant as multiple executable action plans. The results shows some untypical ways of implementing available systems for achieving current safety goals, which may be considered in the further SAM. Moreover, what critical plant resources should be well prepared in advance is also indicated from the case study. Finally, limitation of MFM and the develop OPS system on planning SAM strategies are discussed.

This paper presents a mechanism for developing knowledge based support in multiagent based control and diagnosis. In particular it presents a way for autonomous agents to utilize a qualitative means-ends based model for reasoning about control situations. The proposed mechanism have been used in different scenarios of electric power distribution system protection and control. Results show that agents can use local models of their environment and coordinate with other agents to analyze and understand a disturbance situation and choose an appropriate control action. The paper also introduces Multi Level Flow Modeling (MFM) which has been used to model agent environment and describes development of multiagent implementation of MFM Workbench.

This paper presents a mechanism for developing knowledge based support in multiagent based control and diagnosis. In particular it presents a way for autonomous agents to utilize a qualitative means-ends based model for reasoning about control situations. The proposed mechanism have been used in different scenarios of electric power distribution system protection and control. Results show that agents can use local models of their environment and coordinate with other agents to analyze and understand a disturbance situation and choose an appropriate control action. The paper also introduces Multi Level Flow Modeling (MFM) which has been used to model agent environment and describes development of multiagent implementation of MFM Workbench.

In this paper, a hierarchical management system is proposed to integrate electric vehicles (EVs) into a distribution grid. Three types of actors are included in the system: distribution system operators (DSOs), fleet operators (FOs), and EV owners. In contrast to a typical hierarchical control system where the upper level controller directly controls the lower level subordinated nodes, this study aims to integrate two common indirect control methods: market-based control and price-based control into the hierarchical EVs’ management system. Specifically, on the lower level of the hierarchy, the FOs coordinate the charging behaviors of their EV users using a price-based control method. A parametric utility model is used on the lower level to characterize price elasticity of EVs and thus used by the FO to coordinate the individual EV charging. On the upper level of the hierarchy, the DSO uses the market-based control strategy to coordinate the limited power capacity of power transformer with FOs. To facilitate the application of the two indirect control methods into the system, a model describing decision tasks in control is used to specify the essential functions that are needed in the control system. The simulations illustrate the effectiveness of the proposed solutions.