Search

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.

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.

This paper proposes an approach to categorize electricity suppliers (ESs) for energy trading between ESs and a single aggregator. A principal-agents game model is developed to model the interactions between an aggregator and different categories of ESs by considering the benefits of both parties. In a proposed game, the aggregator as a principal will purchase a certain amount of power from different-category ESs with the cheapest pricing options available, and at the same time the ESs, acting as agents will maximize their utilities by selling their power to the aggregator instead of feeding the grid at a low rate. The developed optimal contract-based scheme, which can be implemented distributed manner, allows different-category ESs to sell their power at different prices based on their unit production cost to maximize their benefits, and the total cost to the aggregator is minimized. Numerical analysis confirms the effectiveness of the proposed ESs categorizing framework in the development of a contract-based incentive mechanism for energy trading.

Advancements in smart-grid technology such as the development of a bi-directional communication infrastructure and smart metering provide an opportunity to reduce energy cost by sharing renewable energy among buildings. A proactive building equipped with renewable energy sources (RESs) can share surplus renewable power (SRP) with neighboring traditional buildings (without RESs) for the optimal utilization of RESs. In this paper, the interaction of a proactive building with neighboring traditional buildings in the context of power sharing based on generation and load demand is considered. Within a given time horizon divided into multiple time steps in which generation and load demand occurs, the proactive buildings may experience a power surplus or deficit. While any deficit can be obtained from the utility grid, the proactive building may consider sharing/selling its unused power with neighboring buildings. An algorithm is developed to manage SRP based on price signals, RESs' production and load demand. The developed algorithm is tested using real-time load and generation data of different buildings situated in Griffith University, Australia. A cost-benefit analysis is also carried out using current electricity charges to show the cost effectiveness of power sharing.

Real-time electricity pricing has been a key issue in Australia's national electricity markets. The average electricity bill paid by a consumer is influenced by the amount of energy consumption by different loads and their time of use. Certain loads such as dishwashers and pool pumps are deferrable while loads such as lighting and refrigeration are non-deferable. HVAC (heating, ventilation and air conditioning) systems contribute to a major part of the energy consumption of a building. This paper discusses the design of real-time energy pricing in a transactive environment where the key players will be the consumers. The designed real-time control scheme in this paper is to define the final thermostat set-point for each HVAC based on the bidding price. In addition, this paper proposes the communication architecture to implement the designed control scheme through two-way communication between HVACs, data aggregator and the central controller. The proposed architecture utilizes the Message Queue Telemetry Transport (MQTT) protocol which is the state-of-the-art lightweight messaging protocol for use on top of the TCP/IP protocol. From the analysis, it is evident that the proposed algorithm enables customers to reduce energy costs and increases energy efficiency.

This paper proposes the installation of a battery energy storage (BES) system along with a PV system in a hierarchical transactive energy management (TEM) framework to reduce consumer's electricity bills. An algorithm is developed to control the charging and discharging cycle of the proposed battery in a TEM system. A new cost benefit analysis (CBA) approach is presented for solar energy systems and combination of photovoltaic (PV) units and BES systems when these are installed in a proactive houses. Economic benefit analysis is based on real time electricity rate and battery cost to give an exact idea of returns and yearly savings to consumers on their investment. Real load and generation data are used for the analysis and based on the developed algorithm the appropriate battery size is determined. When it is integrated with a 4kW PV unit, it reduces the power mismatch between the load demand and solar generation. The result shows that proactive consumers can maximize their savings considerably on solar investment by installing BES systems.