Demand-Response Based Energy Advisor for Household Energy Management

Home energy management systems (HEMS) are set to play a key role in the future smart grid (SG). HEMS concept enables residential customers to actively participate in demand response programs (DR) to control their energy usage, reduce peak demand and therefore contribute to improve the performance and reliability of the grid. The aim of this paper is to propose an energy management strategy for residential end-consumers. In this framework, a demand response strategy is developed to reduce home energy consumption. The proposed algorithm seeks to minimise peak demand by scheduling household appliances operation and shifting controllable loads during peak hours, when electricity prices are high, to off-peak periods, when electricity prices are lower without affecting the customer’s preferences. The overall system is simulated using MATLAB/Simulink and the results demonstrate the effectiveness of the proposed control strategy in managing the daily household energy consumption.Peer reviewe

Demand Response Strategy Based on Reinforcement Learning and Fuzzy Reasoning for Home Energy Management

As energy demand continues to increase, demand response (DR) programs in the electricity distribution grid are gaining momentum and their adoption is set to grow gradually over the years ahead. Demand response schemes seek to incentivise consumers to use green energy and reduce their electricity usage during peak periods which helps support grid balancing of supply-demand and generate revenue by selling surplus of energy back to the grid. This paper proposes an effective energy management system for residential demand response using Reinforcement Learning (RL) and Fuzzy Reasoning (FR). RL is considered as a model-free control strategy which learns from the interaction with its environment by performing actions and evaluating the results. The proposed algorithm considers human preference by directly integrating user feedback into its control logic using fuzzy reasoning as reward functions. Q-learning, a RL strategy based on a reward mechanism, is used to make optimal decisions to schedule the operation of smart home appliances by shifting controllable appliances from peak periods, when electricity prices are high, to off-peak hours, when electricity prices are lower without affecting the customer’s preferences. The proposed approach works with a single agent to control 14 household appliances and uses a reduced number of state-action pairs and fuzzy logic for rewards functions to evaluate an action taken for a certain state. The simulation results show that the proposed appliances scheduling approach can smooth the power consumption profile and minimise the electricity cost while considering user’s preferences, user’s feedbacks on each action taken and his/her preference settings. A user-interface is developed in MATLAB/Simulink for the Home Energy Management System (HEMS) to demonstrate the proposed DR scheme. The simulation tool includes features such as smart appliances, electricity pricing signals, smart meters, solar photovoltaic generation, battery energy storage, electric vehicle and grid supply.Peer reviewe

Named data networking for efficient IoT-based disaster management in a smart campus

Disasters are uncertain occasions that can impose a drastic impact on human life and building infrastructures. Information and Communication Technology (ICT) plays a vital role in coping with such situations by enabling and integrating multiple technological resources to develop Disaster Management Systems (DMSs). In this context, a majority of the existing DMSs use networking architectures based upon the Internet Protocol (IP) focusing on location-dependent communications. However, IP-based communications face the limitations of inefficient bandwidth utilization, high processing, data security, and excessive memory intake. To address these issues, Named Data Networking (NDN) has emerged as a promising communication paradigm, which is based on the Information-Centric Networking (ICN) architecture. An NDN is among the self-organizing communication networks that reduces the complexity of networking systems in addition to provide content security. Given this, many NDN-based DMSs have been proposed. The problem with the existing NDN-based DMS is that they use a PULL-based mechanism that ultimately results in higher delay and more energy consumption. In order to cater for time-critical scenarios, emergence-driven network engineering communication and computation models are required. In this paper, a novel DMS is proposed, i.e., Named Data Networking Disaster Management (NDN-DM), where a producer forwards a fire alert message to neighbouring consumers. This makes the nodes converge according to the disaster situation in a more efficient and secure way. Furthermore, we consider a fire scenario in a university campus and mobile nodes in the campus collaborate with each other to manage the fire situation. The proposed framework has been mathematically modeled and formally proved using timed automata-based transition systems and a real-time model checker, respectively. Additionally, the evaluation of the proposed NDM-DM has been performed using NS2. The results prove that the proposed scheme has reduced the end-to-end delay up from 2% to 10% and minimized up to 20% energy consumption, as energy improved from 3% to 20% compared with a state-of-the-art NDN-based DMS

Policy-based power consumption management in smart energy community using single agent and multi agent Q learning algorithms

Power consumption in residential sector has increased due to growing population, economic growth, invention of many electrical appliances and therefore is becoming a growing concern in the power industry. Managing power consumption in residential sector without sacrificing user comfort has become one of the main research areas recently. The complexity of the power system keeps growing due to the penetration of alternative sources of electric energy such as solar plant, Hydro, Biomass, Geothermal and wind farm to meet the growing demand for electricity. To overcome the challenges due to complexity, the power grid needs to be intelligent in all aspects. As the grid gets smarter and smarter, considerable efforts are being undertaken to make the houses and businesses smarter in consuming the electrical energy to minimize and level the electricity demand which is also known as Demand Side Management (DSM). It also necessitates that the conventional way of modelling, control and energy management in all sectors needs to be enhanced or replaced by intelligent information processing techniques. In our research work, it has been done in several stages. (Purpose of Study and Results) We proposed a policy-based framework which allows intelligent and flexible energy management of home appliances in a smart home which is complex and dynamic in ways that saves energy automatically. We considered the challenges in formalizing the behaviour of the appliances using their states and managing the energy consumption using policies. Policies are rules which are created and edited by a house agent to deal with situations or power problems that are likely to occur. Each time the power problem arises the house agent will refer to policy and one or a set of rules will be executed to overcome that situation. Our policy-based smart home can manage energy efficiently and can significantly participate in reducing peak energy demand (thereby may reduce carbon emission). Our proposed policy-based framework achieves peak shaving so that power consumption adapts to available power, while ensuring the comfort level of the inhabitants and taking device characteristics in to account. Our simulation results on MATLAB indicate that the proposed Policy driven homes can effectively contribute to Demand side power management by decreasing the peak hour usage of the appliances and can efficiently manage energy in a smart home in a user-friendly way. We propounded and developed peak demand management algorithms for a Smart Energy Community using different types of coordination mechanisms for coordination of multiple house agents working in the same environment. These algorithms use centralized model, decentralized model, hybrid model and Pareto resource allocation model for resource allocation. We modelled user comfort for the appliance based on user preference, the power reduction capability and the important activities that run around the house associated with that appliance. Moreover, we compared these algorithms with respect to their peak reduction capability, overall comfort of the community, simplicity of the algorithm and community involvement and finally able to find the best performing algorithm among them. Our simulation results show that the proposed coordination algorithms can effectively reduce peak demand while maintaining user comfort. With the help of our proposed algorithms, the demand for electricity of a smart community can be managed intelligently and sustainably. This work is not only aiming for peak reduction management it aims for achieving it while keeping the comfort level of the inhabitants is minimum. It can learn user’s behaviour and establish the set of optimal rules dynamically. If the available power to a house is kept at a certain level the house agent will learn to use this notional power to operate all the appliances according to the requirements and comfort level of the household. This way the consumers are forced to use the power below the set level which can result in the over-all power consumption be maintained at a certain rate or level which means sustainability is possible or depletion of natural resources for electricity can be reduced. Temporal interactions of Energy Demand by local users and renewable energy sources can also be done more efficiently by having a set of new policy rules to switch between the utility and the renewable source of energy but it is beyond the scope of this thesis. We applied Q learning techniques to a home energy management agent where the agent learns to find the optimal sequence of turning off appliances so that the appliances with higher priority will not be switched off during peak demand period or power consumption management. The policy-based home energy management determines the optimal policy at every instant dynamically by learning through the interaction with the environment using one of the reinforcement learning approaches called Q-learning. The Q-learning home power consumption problem formulation consisting of state space, actions and reward function is presented. The implications of these simulation results are that the proposed Q- learning based power consumption management is very effective and enables the users to have minimum discomfort during participation in peak demand management or at the time when power consumption management is essential when the available power is rationale. This work is extended to a group of 10 houses and three multi agent Q- learning algorithms are proposed and developed for improving the individual and community comfort while at the same time keeping the power consumption below the available power level or electricity price below the set price. The proposed algorithms are weighted strategy sharing algorithm, concurrent Q learning algorithm and cooperative distributive learning algorithm. These proposed algorithms are coded and tested for managing power consumption of a group of 10 houses and the performance of all three algorithms with respect to power management and community comfort is studied and compared. Actual power consumption of a community and modified power consumption curves using Weighted Strategy Sharing algorithm, Concurrent learning and Distributive Q Learning and user comfort results are presented, and the results are analysed in this thesis

The role of off-board EV battery chargers in smart homes and smart grids: operation with renewables and energy storage systems

Concerns about climate changes and environmental air pollution are leading to the adoption of new technologies for transportation, mainly based on vehicle electrification and the interaction with smart grids, and also with the introduction of renewable energy sources (RES) accompanied by energy storage systems (ESS). For these three fundamental pillars, new power electronics technologies are emerging to transform the electrical power grid, targeting a flexible and collaborative operation. As a distinctive factor, the vehicle electrification has stimulated the presence of new technologies in terms of power management, both for smart homes and smart grids. As the title indicates, this book chapter focuses on the role of off-board EV battery chargers in terms of operation modes and contextualization for smart homes and smart grids in terms of opportunities. Based on a review of on-board and off-board EV battery charging systems (EV-BCS), this chapter focus on the off-board EV-BCS framed with RES and ESS as a dominant system in future smart homes. Contextualizing these aspects, three distinct cases are considered: (1) An ac smart home using separate power converters, according to the considered technologies; (2) A hybrid ac and dc smart home with an off-board EV-BCS interfacing RES and ESS, and with the electrical appliances plugged-in to the ac power grid; (3) A dc smart home using a unified 2 off-board EV-BCS with a single interface for the electrical power grid, and with multiple dc interfaces (RES, ESS, and electrical appliances). The results for each case are obtained in terms of efficiency and power quality, demonstrating that the off-board EV-BCS, as a unified structure for smart homes, presents better results. Besides, the off-board EV-BCS can also be used as an important asset for the smart grid, even when the EV is not plugged-in at the smart home.(undefined