Bishnu Prasad Bhattarai

Optimizing Electric Vehicle Coordination Over a Heterogeneous Mesh Network in a Scaled-Down Smart Grid Testbed

High penetration of renewable energy sources and electric vehicles (EVs) create power imbalance and congestion in the existing power network, and hence causes significant problems in the control and operation. Despite investing huge efforts from the electric utilities, governments, and researchers, smart grid (SG) is still at the developmental stage to address those issues. In this regard, a smart grid testbed (SGT) is desirable to develop, analyze, and demonstrate various novel SG solutions, namely demand response, real-time pricing, and congestion management. In this paper, a novel SGT is developed in a laboratory by scaling a 250 kVA, 0.4 kV real low-voltage distribution feeder down to 1 kVA, 0.22 kV. Information and communication technology is integrated in the scaled-down network to establish real-time monitoring and control. The novelty of the developed testbed is demonstrated by optimizing EV charging coordination realized through the synchronized exchange of monitoring and control packets via an heterogeneous Ethernet-based mesh network.

Demand flexibility from residential heat pump

Demand response (DR) is considered as a potentially effective tool to compensate generation intermittency imposed by renewable sources. Further, DR can instigate to offer optimum asset utilization and to avoid or delay the need for new infrastructure investment. Being a sizable load together with high thermal time constant, heat pumps (HP) can offer a great deal of flexibility in the future intelligent grids especially to compensate fluctuating generation. However, the HP flexibility is highly dependent on thermal demand profile, namely hot water and space heating demand. This paper proposes price based scheduling followed by a demand dispatch based central control and a local voltage based adaptive control, to realize HP demand flexibility. Two-step control architecture, namely local primary control encompassed by the central coordinative control, is proposed to implement the aforementioned control techniques. Results show that HP flexibility can contribute significantly to both the local network and system level balancing.

Hierarchical control architecture for demand response in smart grids

To compensate for intermittency of generation and consequent impacts of non-dispatchable generating sources, especially solar PV panels and wind turbines, demand response (DR) has been considered one of the most effective tools. In recent years, DR has received more attention as a potentially effective tool for optimum asset utilization and to avoid or delay the need for new infrastructure investment. Furthermore, most of the power networks are under the process of reconfiguration to realize the concept of smart grid and are at the transforming stage to support various forms of DR. However, a number of issues, including DR enabling technologies, control strategy, and control architecture, are still under discussion. This paper outlines novel control requirements based on the categorization of existing DR techniques. More specifically, the roles and responsibilities of smart grid actors for every DR category are allotted and their mode of interactions to coordinate individual as well as coordinative goals is described. Next, hierarchical control architecture (HCA) is developed for the overall coordination of control strategies for individual DR categories. The involved issues are discussed and compared.

Two-stage electric vehicle charging coordination in low voltage distribution grids

Increased environmental awareness in the recent years has encouraged rapid growth of renewable energy sources (RESs); especially solar PV and wind. One of the effective solutions to compensate intermittencies in generation from the RESs is to enable consumer participation in demand response (DR). Being a sizable rated element, electric vehicles (EVs) can offer a great deal of demand flexibility in future intelligent grids. This paper first investigates and analyzes driving pattern and charging requirements of EVs. Secondly, a two-stage charging algorithm, namely local adaptive control encompassed by a central coordinative control, is proposed to realize the flexibility offered by EV. The local control enables adaptive charging; whereas the central coordinative control prepares optimized charging schedules. Results from various scenarios show that the proposed algorithm enables significant penetration, up to 66%, of EV in a typical low voltage distribution grids and acts as a potential resource to delay or defer grid reinforcement.

Voltage controlled dynamic demand response

Future power system is expected to be characterized by increased penetration of intermittent sources. Random and rapid fluctuations in demands together with intermittency in generation impose new challenges for power balancing in the existing system. Conventional techniques of balancing by large central or dispersed generations might not be sufficient for future scenario. One of the effective methods to cope with this scenario is to enable demand response. This paper proposes a dynamic voltage regulation based demand response technique to be applied in low voltage (LV) distribution feeders. An adaptive dynamic model has been developed to determine composite voltage dependency of an aggregated load on feeder level. Following the demand dispatch or control signal, optimum voltage setting at the LV substation is determined based on the voltage dependency of the load. Furthermore, a new technique has been proposed to estimate the voltage at consumer point of connection (POC) to ensure operation within voltage limits. Finally, the effectiveness of the proposed method is analyzed comprehensively with reference to three different scenarios on a low voltage (LV) feeder (Borup feeder) owned by Danish electricity distribution company SEAS-NVE.

Smart Grid Constraint Violation Management for Balancing and Regulating Purposes

The gradual active load penetration in low-voltage distribution grids is expected to challenge their network capacity in the near future. Distribution system operators should for this reason resort to either costly grid reinforcements, use of low-voltage boosters, or demand response (DR) mechanisms. Since DR implementation is usually more cost effective, it is the favorable solution to avoid or delay the need for grid reinforcement. To this end, this paper presents a framework for handling grid limit violations, both voltage and current, to ensure a secure and qualitative operation of the distribution grid. This framework consists of two steps, namely a proactive centralized, and subsequently, a reactive decentralized control scheme. The former is employed to balance the 1-h-ahead load, while the latter aims at regulating the consumption in real time. In both schemes, fairness in terms of utilization of demand flexibility among the customers is incorporated. It is demonstrated that the proposed methodology aids in keeping the grid status within preset limits while utilizing flexibility from all flexibility participants.

Optimum Aggregation and Control of Spatially Distributed Flexible Resources in Smart Grid

This paper presents an algorithm to optimally aggregate spatially distributed flexible resources at strategic microgrid/smart-grid locations. The aggregation reduces a distribution network having thousands of nodes to an equivalent network with a few aggregated nodes, thereby enabling distribution system operators (DSOs) to make faster operational decisions. Moreover, the aggregation enables flexibility from small distributed flexible resources to be traded to different power and energy markets. A hierarchical control architecture comprising a combination of centralized and decentralized control approaches is proposed to practically deploy the aggregated flexibility. The proposed method serves as a great operational tool for DSOs to decide the exact amount of required flexibilities from different network section(s) for solving grid constraint violations. The effectiveness of the proposed method is demonstrated through simulation of three operational scenarios in a real low voltage distribution system having high penetrations of electric vehicles and heat pumps. The simulation results demonstrated that the aggregation helps DSOs not only in taking faster operational decisions, but also in effectively utilizing the available flexibility.

Active control of thermostatic loads for economic and technical support to distribution grids

Active control of electric water heaters (EWHs) is presented in this paper as a means of exploiting demand flexibility for supporting low-voltage (LV) distribution grids. A single-node model of an EWH is implemented in DIgSILENT PowerFactory using a thermal energy balancing equation and three decentralized control schemes are designed to ensure consumer comfort, economic benefit to the consumer, and technical support to LV grids. First, a price-based control that adaptively adjusts an allowable energy band per electricity price is implemented to ensure economic benefit. Next, an adaptive update of the energy band is done based on feeder loading to respect thermal grid constraints. Finally, a voltage-based control is implemented to provide real-time voltage support to the LV grids. Simulation results demonstrate the capability of the presented method to realize both economic and technical advantages. For the given configuration and pricing scheme, EWH owners are able to decrease their electricity cost by 29.33%, along with simultaneous assurance of consumer comfort and grid constraints.

An adaptive overcurrent protection in smart distribution grid

High penetration of distributed energy resources (DERs) creates various protection challenges, such as protection blinding, false tripping, unsynchronized reclosing, etc. Additionally, adaptation of active network management approaches namely demand response and network reconfiguration also threats existing protection practice. In this study, a combination of a local adaptive and communication assisted central protection is proposed, whereby the relay settings are dynamically updated based on online identification of the network topologies and status of DERs. Particularly, the local adaptive protection updates relay settings based on DERs status (ON/OFF) employing locally acquired information, whereas the centralized protection updates the relay settings during major changes in grid topologies: network reconfiguration and switching between islanded and grid-connected modes. The effectiveness of the proposed algorithm is demonstrated through simulation results performed on a medium voltage (10 kV) distribution network using real time digital simulation (RTDS). Simulation results demonstrates that the proposed algorithm provides good discrimination and selectivity of faults during varied network conditions.

Improving and handling electric vehicle penetration level by different smart charging algorithms in distribution grids

This paper proposes different smart charging algorithms for electric vehicles (EVs) to find out the maximum grid capability in dealing with these new devices. The main objective is to obtain maximum EV penetration in the distribution grid without reinforcing the grid in order to avoid any cost for distribution system operators (DSOs). Two smart charging algorithms are proposed in this study. The proposed algorithms are applied to a part of the Danish distribution grid as a case study. As a comparison, a dumb charging scenario, i.e. charging EVs without any specific order or algorithm, is also simulated. Simulation results demonstrate the capability of the smart charging methods to increase the penetration of EVs up to three times, compared to the base case.