A Coordinated Electric Vehicle Management System for Grid-Support Services in Residential Networks

The increased integration of light-duty electric vehicles (EVs) into low-voltage (LV) residential networks imposes capacity issues for the grid operators. For example, the uncoordinated and clustered charging of residential EVs can often overload grid assets, jeopardize network reliability, and violate local voltage constraints. This article proposes a coordinated management system for EVs in LV residential networks with power grid support functionalities to address grid overloading and local voltage constraints violation. The charging and discharging of EV batteries in the network are coordinated via a local EV aggregator. The coordination is realized using multiagent system architecture that provides the EV owners with full decision-making authority and preserves their privacy. The EV coordination and vehicle-to-grid (V2G) resource optimization of the EV aggregator is formulated as a mixed-integer programming-based optimization model to minimize the electricity cost for the EV owners based on a real-time tariff while complying with local grid constraints. The proposed methodology is evaluated via simulation of an LV residential network in Sydney, Australia with actual load demand data. The simulation results indicate the efficacy of the proposed strategy for the electricity cost reduction of the EV owners while mitigating grid overloading and maintaining desired bus voltages

Advanced power routing framework for optimal economic operation and control of solar photovoltaic-based islanded microgrid

© 2019 Institution of Engineering and Technology. All rights reserved. Energy sharing through a microgrid (MG) is essential for islanded communities to maximise the use of distributed energy resources (DERs) and battery energy storage systems (BESSs). Proper energy management and control strategies of such MGs can offer revenue to prosumers (active consumers with DERs) by routing excess energy to their neighbours and maintaining grid constraints at the same time. This paper proposes an advanced power-routing framework for a solarphotovoltaic (PV)-based islanded MG with a central storage system (CSS). An optimisation-based economic operation for the MG is developed that determines the power routing and energy sharing in the MG in the day-ahead stage. A modified droop controller-based real-time control strategy has been established that maintains the voltage constraints of the MG. The proposed power-routing framework is verified via a case study for a typical islanded MG. The outcome of the optimal economic operation and a controller verification of the proposed framework are presented to demonstrate the effectiveness of the proposed powerrouting framework. Results reveal that the proposed framework performs a stable control operation and provides a profit of 57 AU$/day at optimal conditions

EV Scheduling Framework for Peak Demand Management in LV Residential Networks

Increased electrification in the residential and transport sectors is changing the energy demand profiles significantly, which results in reshaped peak demand. These changes in demand profiles can cause grid overloading and jeopardize network reliability especially when the excessive use of electricity within a network is uncoordinated. In this article, an aggregated coordination mechanism is proposed for electric vehicle (EV) charge–discharge scheduling to manage the peak demand in the low-voltage (LV) residential networks. The proposed model uses mixed-integer-programming-based optimization approach to minimize the cost of energy while managing the peak demand and complying with grid constraints. A stochastic model is presented to account for the uncertainties associated with forecast inaccuracies of the day-ahead scheduling. The proposed strategy is assessed by means of simulation studies considering an LV residential neighborhood in Sydney, Australia. The results indicate the effectiveness of the proposed strategy to minimize the cost of electricity for the EV owners while managing the peak demand for the grid operators. Comparison with the state-of-the-art EV scheduling strategies indicates that the proposed strategy can improve the load factor of the local network up to 36%, the peak-to-average ratio up to 27%, and cost reductions up to 56%

Multiagent-Based Transactive Energy Management Systems for Residential Buildings with Distributed Energy Resources

© 2005-2012 IEEE. Proper management of building loads and distributed energy resources (DER) can offer grid assistance services in transactive energy (TE) frameworks besides providing cost savings for the consumer. However, most TE models require building loads and DER units to be managed by external entities (e.g., aggregators), and in some cases, consumers need to provide critical information related to their electricity demand and usage, which hampers their privacy. This article introduces a transactive energy management framework for the buildings in a residential neighborhood to address grid overloading and cost optimization of the buildings. The decentralized coordination for the energy management system is realized by using a multiagent system architecture, which provides the consumers with full decision-making authority and preserves their privacy. A new event-triggered transactive market algorithm is developed, where the buildings trade energy to maximize profits, while the regional grid operator procures energy-supply flexibility of active consumers to prevent transformer overloading. A two-stage energy management system is developed for the residential buildings that schedules building loads and DER units in day-ahead stage to minimize cost and inconveniences for the consumer while participating in the real-time transactive market to maximize profits. An optimal bidding model is developed for the buildings that incorporates the degradation of residential storage devices for energy trading. Case studies and analyses with actual Australian building data and electricity tariff structures indicate the efficacy of the proposed methodology for effective mitigation of transformer overloading at a negligible cost compared to transformer replacement cost. Results also indicate that the proposed system can provide 15-20% cost savings for the consumers while minimizing their inconveniences and degradation of storage devices

Game-theoretic approach to demand-side energy management for a smart neighbourhood in Sydney incorporating renewable resources

Recent developments in smart grid technologies have enabled interactions between energy suppliers and consumers, leading to profits made by both parties via Demand-side management (DSM). Demand-side management enables consumers to control their energy profile to reap economic benefits. It also helps energy providers to reduce the peak average ratio (PAR) by leveraging the flexibility of distributed energy resources (DERs) and renewable-energy resources (RESs) to supplement grid power, thereby avoiding the use of expensive peak-power plants. This paper presents an improved game-theoretic DSM framework for a neighbourhood area to provide cost savings for the consumer and reduce the PAR for the neighbourhood. The proposed DSM framework utilizes the flexibility of DERs and RESs to allow energy sharing among neighbours to reduce the demand peaks. A novel real-time price (RTP) retail tariff model has been established based on historical and predicted wholesale prices. A Nash-game-theory-based optimization model is developed for scheduling the building loads and DERs. The optimization model minimizes the energy cost to the consumer while maintaining an optimal comfort level for the consumer and satisfying consumption constraints to reduce peak demand. The proposed DSM framework and optimization model is verified via case studies with real building consumption data for a neighbourhood in Sydney, Australia. Game-theoretical analysis ensures that users do not make profits if they deviate from their assigned consumption pattern. The performance of various algorithms is evaluated and their effects on the peak average ratio (PAR) and energy costs are discussed. The effectiveness of the proposed game-theoretic optimization model is validated and compared with traditional non-game-theoretic models. The results of the proposed algorithm show reductions in the peak average ratio of the community and the cost incurred by the consumers. The PARs of the game-theoretic approach during summer and winter are 1.76 and 1.81 respectively. The cost reduction of the game-theoretic model is 9.17% during summer and 9.68% during winter compared to the non-cooperative approach. The numerical results represent the efficacy of the proposed DSM model in reducing the PAR of the community and the energy cost to the consumer

A Nested Transactive Energy Market Model to Trade Demand-Side Flexibility of Residential Consumers

© 2010-2012 IEEE. A nested transactive energy (TE) market methodology is presented in this paper for the effective utilization of demand-side flexibility of small-scale residential consumers. The consumers' flexibilities are traded in a local flexibility market to prevent transformer overloading, whereas the demand-side flexibilities are traded in an event-triggered central wholesale demand response market after successive aggregation in the intermediate layers. A two-stage optimization-based scheduling model is presented to optimize the transactive bidding of residential consumers with on-site distributed energy resources and controllable loads. The optimal market methodologies are presented for the integrated TE markets to ensure economic trading for all involved stakeholders. The proposed methodology is numerically validated by simulation studies for different consumer participation levels, and the case studies indicate the efficacy of the proposed methodology for economically feasible procurement of consumer flexibility for transformer overloading and wholesale peak-price events. Results also illustrate that the proposed method offers 2.8-14 times more profits to the participating consumers than the energy-supply incentives according to existing retail tariff structures even considering their thermal discomfort and cycle-aging of storage units for the flexibility support

Output Feedback Adaptive Control for Inter-area Oscillation Damping under Power System Uncertainties

© 2019 IEEE. The power system is inherently a complex nonlinear system and experiences continuous changes in operating conditions due to sudden variations in load demand. The increasing integration of renewable power sources in current grids brings new dynamics and increases complexity in developing reliable control strategies. In addition, the variability of renewable generation introduce uncertainty and therefore, an advanced controller is required to ensure the systems stability. The wide area measurement systems (WAMS) has made the remote signal much readily available, thus improving the overall systems observability. With considering the changing systems dynamics, an output feedback model reference adaptive damping controller is designed and implemented in this paper. The results show the controllers effectiveness to handle the parametric and nonparametric uncertainties of the system while obtaining satisfactory damping action on inter-area oscillations

Reverse logistics network design for waste solar photovoltaic panels: A case study of New South Wales councils in Australia.

Waste solar photovoltaic (PV) panels are considered as one of the fastest-growing future waste streams under the category of large electronic waste (e-waste). The lifespan of solar panels varies from 20 to 30 years, and an appropriate reverse logistics network design is essential to manage the waste stream efficiently once their lifetime expires. Mixed-integer programming-based RL model is proposed in this paper for New South Wales, Australia that minimizes the overall cost by identifying optimal locations and sizing of the collection points while determining optimal capacities for recycling facilities. Using the historical data (2001-2017) on the installed capacity of solar panels in the state, the potential waste generation (at council-level) is estimated and optimized solutions are proposed for the year 2047. The results of the study show that the highest waste solar PV will be generated at Murrumbidgee, Berrigan, Balranald, and Bogan councils. Out of 129 councils in the state, the model identifies 78 optimized-locations of the collection points that would be required in the councils. In the councils of Newcastle, Narrandera and Wagga Wagga, three major recycling facilities would need to be established. This is the first systematic attempt in designing an optimized RL network in Australia focusing on waste solar PV. Policy-makers will find this research highly valuable in decision-making on local recycling infrastructure development

On the application of Home Energy Management Systems for power grid support

© 2019 Elsevier Ltd Home Energy Management Systems (HEMSs) are being implemented for residential energy management in various parts of the world. Conventionally, a HEMS is developed from the consumer's perspective, with the principal aim of cost-saving while maintaining optimal consumers' comfort. In recent years, various Demand Response programs are being incorporated into HEMSs to address the power grid constraints. In this paper, the functionality of grid support through the HEMSs is presented. The developed scheme utilizes an agent-based coordination mechanism in an active distribution network and manages the household appliances to comply with thermal and voltage constraints of the grid. The proposed mechanism is evaluated through simulation of a typical Dutch low-voltage (LV) residential feeder. A hardware prototype has also been developed and tested in the laboratory environment. The proposed methodologies show promising perspectives for local voltage-violation support and direct load control for congestion management of the grid