The growing demand for electricity, coupled with increased efficiency requirements, creates new opportunities for the development of demand-side management systems. Here we describe an approach for load allocation among different classes of device. We adopt the concept of strategic choice to determine the optimal strategy for a given situation. Electricity resources are allocated based on demand, priority, fairness, the available electrical resources, and the budget, so that even when the unit price is high (i.e., the available resources are restricted), higher-priority devices continue to operate without interruption. When the price falls, resources are distributed to satisfy the requirements of a larger number of devices. We include ESSs (energy storage systems) in the algorithm to reserve energy during low-price times for use during high-price times. The algorithm described here can be used to allocate resources among heterogeneous devices, and has potential not only to reduce peak demand but also to increase the overall efficiency of the system.

Loesch, M.; Hufnagel, D. ; Steuer, S. ; Fabnacht, T. ; Schmeck, H.

A major component of the future Smart Grid is an adaptive demand side that allows to handle the fluctuating power supply based on renewable energies. In this paper, we present an evolutionary algorithm that allows for shifting electrical loads generated by heat pumps. Our approach is based on overheating the hot water storage in order to get a higher degree of freedom for scheduling. In our scenario, we assume time-variable price and load limitation signals as well as a prediction for local power generation from photovoltaic panels to incentivize the load shifting. Using these signals, we consider the future thermal demand to schedule the heat pump such that electricity costs are decreased. Our simulations show that heat pumps and hot water storages bear potential to shift loads over a time span of up to multiple hours, thus providing economical storage capacity. In doing so and based on electricity prices from the stock exchange, we were able to significantly decrease electricity costs for operating the heat pump.

Published in: Intelligent Energy and Power Systems (IEPS), 2014 IEEE International Conference on

Oliver Parson, is a research fellow in the Agents, Interaction and Complexity Group within Electronics and Computer Science at the University of Southampton. He is interested in investigating the ways in which machine learning techniques can be used to break down household energy consumption data into individual appliances, also known as Non-intrusive Appliance Load Monitoring (NIALM) or energy disaggregation.

Full-Function Web-Enabled Manuscript Submission and Tracking System for Peer Review

Cecati, C. ; Citro, C. ; Siano, P.

The integration of renewable energy systems (RESs) in smart grids (SGs) is a challenging task, mainly due to the intermittent and unpredictable nature of the sources, typically wind or sun. Another issue concerns the way to support the consumers' participation in the electricity market aiming at minimizing the costs of the global energy consumption. This paper proposes an energy management system (EMS) aiming at optimizing the SG's operation. The EMS behaves as a sort of aggregator of distributed energy resources allowing the SG to participate in the open market. By integrating demand side management (DSM) and active management schemes (AMS), it allows a better exploitation of renewable energy sources and a reduction of the customers' energy consumption costs with both economic and environmental benefits. It can also improve the grid resilience and flexibility through the active participation of distribution system operators (DSOs) and electricity supply/demand that, according to their preferences and costs, respond to real-time price signals using market processes. The efficiency of the proposed EMS is verified on a 23-bus 11-kV distribution network.

Sustainable Energy, IEEE Transactions on (Volume:2 , Issue: 4 ) - 2001

Albert Y. Zomaya and Young Choon Lee, Chen Wang and Martin De Groot