Stochastic interval-based optimal offering model for residential energy management systems by household owners

This paper proposes an optimal bidding strategy for autonomous residential energy management systems. This strategy enables the system to manage its domestic energy production and consumption autonomously, and trade energy with the local market through a novel hybrid interval-stochastic optimization method. This work poses a residential energy management problem which consists of two stages: day-ahead and real-time. The uncertainty in electricity price and PV power generation is modeled by interval-based and stochastic scenarios in the day-ahead and real-time transactions between the smart home and local electricity market. Moreover, the implementation of a battery included to provide energy flexibility in the residential system. In this paper, the smart home acts as a price-taker agent in the local market, and it submits its optimal offering and bidding curves to the local market based on the uncertainties of the system. Finally, the performance of the proposed residential energy management system is evaluated according to the impacts of interval optimistic and flexibility coefficients, optimal bidding strategy, and uncertainty modeling. The evaluation has shown that the proposed optimal offering model is effective in making the home system robust and achieves optimal energy transaction. Thus, the results prove that the proposed optimal offering model for the domestic energy management system is more robust than its non-optimal offering model. Moreover, battery flexibility has a positive effect on the system’s total expected profit. With regarding to the bidding strategy, it is not able to impact the smart home’s behavior (as a consumer or producer) in the day-ahead local electricity market.This work is supported by the European Commission H2020 MSCA-RISE-2014: Marie Sklodowska-Curie project DREAM-GO Enabling Demand Response for short and real-time Efficient And Market Based Smart Grid Operation—An intelligent and real-time simulation approach Ref. 641794, and Grant Agreement No. 703689 (Project ADAPT). Moreover, Amin Shokri Gazafroudi acknowledge the support by the Ministry of Education of the Junta de Castilla y León and the European Social Fund through a grant from predoctoral recruitment of research personnel associated with the research project "Arquitectura multiagente para la gestión eficaz de redes de energía a través del uso de técnicas de intelligencia artificial" of the University of Salamanca. Moreover, authors would like to thank Dr. Juan Miguel Morales González from University of Malaga for his thoughtful suggestions.info:eu-repo/semantics/publishedVersio

Power and energy visualization for the micro-management of household electricity consumption

The paper describes a pilot system for the detailed management of domestic electricity consumption aimed at minimizing demand peaks and consumer cost. Management decisions are made both interactively by consumers themselves, and where practical, automatically by computer. These decisions are based on realtime pricing and availability information, as well as current and historic usage data. The benefits of the energy strategies implied by such a system are elaborated, showing the potential for significant peak demand reduction and slowing of the need for growth in generation capacity. An overview is provided of the component technologies and interaction methods we have designed, but the paper focuses on the communication of real-time information to the consumer through a combination of specific and ambient visualizations. There is a need for both overview information (eg how much power is being used right now; how much energy have we used so far today; what does it cost?) and information at the point-of-use (is it OK to turn this dryer on now, or should I wait until later?). To assist the design of these visualizations, a survey is underway aimed at establishing people's understanding of power and energy concepts

An Agent Based Simulation of Smart Metering Technology Adoption

Based on the classic behavioural theory “the Theory of Planned Behaviour”, we develop an agent-based model to simulate the diffusion of smart metering technology in the electricity market. We simulate the emergent adoption of smart metering technology under different management strategies and economic regulations. Our research results show that in terms of boosting the take-off of smart meters in the electricity market, choosing the initial users on a random and geographically dispersed basis and encouraging meter competition between energy suppliers can be two very effective strategies. We also observe an “S-curve” diffusion of smart metering technology and a “lock-in” effect in the model. The research results provide us with insights as to effective policies and strategies for the roll-out of smart metering technology in the electricity market

A three-dimensional model of residential energy consumer archetypes for local energy policy design in the UK

This paper reviews major studies in three traditional lines of research in residential energy consumption in the UK, i.e. economic/infrastructure, behaviour, and load profiling. Based on the review the paper proposes a three-dimensional model for archetyping residential energy consumers in the UK by considering property energy efficiency levels, the greenness of household behaviour of using energy, and the duration of property daytime occupancy. With the proposed model, eight archetypes of residential energy consumers in the UK have been identified. They are: pioneer greens, follower greens, concerned greens, home stayers, unconscientious wasters, regular wasters, daytime wasters, and disengaged wasters. Using a case study, these archetypes of residential energy consumers demonstrate the robustness of the 3-D model in aiding local energy policy/intervention design in the UK

Analysis of the gamification applications to improve the energy savings in residential buildings

This paper proposes a set of metrics to evaluate and compare applications in a new but quickly developing field – energy management software (EMS) in residential buildings. The goal of the paper is to highlight tendencies and to detect drawbacks of pre sent applications to develop a new one taking into account the results of previous analysis. It shows a shortlist of applications examined. Provides the conclusion drawing to the metrics and proposes mai n issues to be considered in the development of a new application.Peer ReviewedPostprint (author's final draft

Islanded house operation using a micro CHP

The µCHP is expected as the successor of\ud the conventional high-efficiency boiler producing next to\ud heat also electricity with a comparable overall efficiency.\ud A µCHP appliance saves money and reduces greenhouse\ud gas emission.\ud An additional functionality of the µCHP is using the\ud appliance as a backupgenerator in case of a power outage.\ud The µCHPcould supply the essential loads, the heating and\ud reduce the discomfort up to a certain level. This requires\ud modifications on the µCHP appliance itself as well as on\ud the domestic electricity infrastructure. Furthermore some\ud extra hardware and a control algorithm for load balancing\ud are necessary.\ud Our load balancing algorithm is supposed to start and\ud stop the µCHP and switch off loads if necessary. The first\ud simulation results show that most of the electricity usage\ud is under the maximum generation line, but to reduce the\ud discomfort an electricity buffer is required.\u

On the Comparison of Stochastic Model Predictive Control Strategies Applied to a Hydrogen-based Microgrid

In this paper, a performance comparison among three well-known stochastic model predictive control approaches, namely, multi-scenario, tree-based, and chance-constrained model predictive control is presented. To this end, three predictive controllers have been designed and implemented in a real renewable-hydrogen-based microgrid. The experimental set-up includes a PEM electrolyzer, lead-acid batteries, and a PEM fuel cell as main equipment. The real experimental results show significant differences from the plant components, mainly in terms of use of energy, for each implemented technique. Effectiveness, performance, advantages, and disadvantages of these techniques are extensively discussed and analyzed to give some valid criteria when selecting an appropriate stochastic predictive controller.Ministerio de Economía y Competitividad DPI2013-46912-C2-1-RMinisterio de Economía y Competitividad DPI2013-482443-C2-1-