Spatiotemporal Arbitrage of Large-Scale Portable Energy Storage for Grid Congestion Relief

Energy storage has great potential in grid congestion relief. By making large-scale energy storage portable through trucking, its capability to address grid congestion can be greatly enhanced. This paper explores a business model of large-scale portable energy storage for spatiotemporal arbitrage over nodes with congestion. We propose a spatiotemporal arbitrage model to determine the optimal operation and transportation schedules of portable storage. To validate the business model, we simulate the schedules of a Tesla Semi full of Tesla Powerpack doing arbitrage over two nodes in California with local transmission congestion. The results indicate that the contributions of portable storage to congestion relief are much greater than that of stationary storage, and that trucking storage can bring net profit in energy arbitrage applications.Comment: Submitted to IEEE PES GM 2019; 5 pages,4 figure

Hosting Capacity Assessment of Distribution Systems

The increasing penetration of distributed energy resources (DERs) in distribution systems may result in a number of technical problems such as over-voltage, overloading, maloperation of protection systems and power quality issues. One approach to address the above-mentioned issues is upgrading the distribution network, which is quite costly. The second approach is to limit the penetration of DERs to the hosting capacity (HC), which is defined as the maximum DER capacity that can be installed in a system without violating the operational constraints. Understanding this concept can assist utilities to ensure the reliable operation of the system. There have been different studies to identify the HC in a system. Nevertheless, the uncertainties associated with the DERs and loads have not been addressed properly in such studies. Besides, it is very difficult to quantify the findings of those studies and make general conclusions, as they were often based on specific networks, while their methods is time consuming in a big distribution network. Furthermore, the impact of voltage control schemes and emerging technologies, such as electric vehicles (EVs) and battery energy storage systems (BESSs) on the HC have not been studied, adequately. Thus, in this thesis, we propose a suitable HC assessment framework, as well as utilize some of the conventional and emerging resources to increase the HC

Advanced Control of Active Distribution Networks Integrating Dispersed Energy Storage Systems

Due to the increased penetration of Distributed Generations (DGs) in distribution networks, the system control and operation may become quite different from the case of traditional network. Most DGs can only provide intermittent power to the Active Distribution Networks (ADNs) due to the intermittent nature of the resources. Moreover, ADN utilities usually do not own DGs, and have difficulty in controlling directly DGs output powers. The main problem related to the considerable connection of DGs is usually associated to the node voltage quality and line congestion mitigation. Within the above context, the motivating factors for this thesis are supported by the issues related to optimal operation and control of ADNs integrating stochastic and non-stochastic DGs. One of the most promising near-term solution is offered by using distributed Energy Storage Systems (ESSs) which can perform their full role to guarantee a more flexible network. Indeed, the availability of ESSs allows, in principle, to: (i) actively control the power flows into the grid, (ii) indirectly control the voltage profiles along the network feeders and (iii) locally balance the hour/daily and weekly load variations. In this thesis, ESSs are assumed to be the only controllable devices in ADNs. As a result, DGs can be indirectly controlled by means of ESSs. First, this manuscript presents control-oriented model for ESSs. In this respect, the accurate estimation of ESS behavior is utmost important. A generic charge representative model for any ESSs is proposed. Moreover, an improvement of the most common electric equivalent circuit models for the two selected ESSs with different characteristics (namely supercapacitors and batteries) is provided for the development of specific control schemes. They are based on the modeling of redistribution of charges that characterizes the dynamic behaviors of the two devices during long time charging/discharging and relaxation phases. Second, this manuscript presents advanced control/scheduling algorithm for ADNs. The operation and control of ADNs can be achieved either centrally or in a decentralized way. The amount of information to be centrally treated would considerably grow due to the number of generation equipmentâs inserted into the grid and the stochastic operation nature of some of them. This consideration introduces the idea that some ADN operation problems, such as voltage control or line congestion mitigation, can be solved in a distributed manner which would help to relieve the information processing burden and to enhance the system security while preventing unwanted event from propagating through the grid. Therefore, the decentralized schemes are considered subdividing the network into quasi-autonomous areas. To this end, given a set of ESSs optimally located in a balanced and radial ADN, this thesis proposes a network partitioning strategy for the optimal voltage control of ADNs. Thus, the network is decomposed into several areas; each under the control of one ESS which has maximum influence on its corresponding area. Based on this clustering, decentralized scheduling strategies and real-time decentralized control algorithms for the clustered ADNs are proposed. The proposed zonal control capability focuses on voltage control and line congestion management. In both proposed decentralized scheduling and real-time control algorithms the communication among different areas is defined using the concept of Multi-Agent Systems