Customer Engagement Plans for Peak Load Reduction in Residential Smart Grids

In this paper, we propose and study the effectiveness of customer engagement plans that clearly specify the amount of intervention in customer's load settings by the grid operator for peak load reduction. We suggest two different types of plans, including Constant Deviation Plans (CDPs) and Proportional Deviation Plans (PDPs). We define an adjustable reference temperature for both CDPs and PDPs to limit the output temperature of each thermostat load and to control the number of devices eligible to participate in Demand Response Program (DRP). We model thermostat loads as power throttling devices and design algorithms to evaluate the impact of power throttling states and plan parameters on peak load reduction. Based on the simulation results, we recommend PDPs to the customers of a residential community with variable thermostat set point preferences, while CDPs are suitable for customers with similar thermostat set point preferences. If thermostat loads have multiple power throttling states, customer engagement plans with less temperature deviations from thermostat set points are recommended. Contrary to classical ON/OFF control, higher temperature deviations are required to achieve similar amount of peak load reduction. Several other interesting tradeoffs and useful guidelines for designing mutually beneficial incentives for both the grid operator and customers can also be identified

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

Exploring the benefits of productization in the utilities sector

none3noThe adoption of Product-Service Systems (PSS) in a business strategy is often mainly associated with the servitization process, where a service component is added to the product component in order to improve the value proposition of the company and better satisfy the customer's needs. The productization phenomenon is far less studied in literature, but growingly prominent in today's market. In particular, companies in the utilities sector have been exploring the potentialities of productization and proposing new business models for improving their offer to the customers, in order to be more and more competitive on the market. In this paper, we provide a first analysis and classification of productization strategies in the utilities sector, starting from experiences in the Italian market, with the aim of understanding which can be the main benefits of a PSS approach in this field, considering the effects on the three dimensions of sustainability (economic, environmental, and social).openElia V.; Gnoni M.G.; Tornese F.Elia, V.; Gnoni, M. G.; Tornese, F

Coordinated Demand Response and Distributed Generation Management in Residential Smart Microgrids

Nowadays with the emerging of small-scale integrated energy systems (IESs) in form of residential smart microgrids (SMGs), a large portion of energy can be saved through coordinated scheduling of smart household devices and management of distributed energy resources (DERs). There are significant potentials to increase the functionality of a typical demand-side management (DSM) strategy, and typical implementation of building-level DERs by integrating them into a cohesive, networked package that fully utilizes smart energy-efficient end-use devices, advanced building control/automation systems, and an integrated communications architecture to efficiently manage energy and comfort at the end-use location. By the aid of such technologies, residential consumers have also the capability to mitigate their energy costs and satisfy their own requirements paying less attention to the configuration of the energy supply system. Regarding these points, this chapter initially defines an efficient framework for coordinated DSM and DERs management in an integrated building and SMG system. Then a working energy management system (EMS) for applications in residential IESs is described and mathematically modeled. Finally, the effectiveness and applicability of the proposed model is tested and validated in different operating modes compared to the existing models. The findings of this chapter show that by the use of an expert EMS that coordinates supply and demand sides simultaneously, it is very possible not only to reduce energy costs of a residential IES, but also to provide comfortable lifestyle for occupants

Design and Development IoT based Smart Energy Management Systems in Buildings through LoRa Communication Protocol

Energy management is a vital tool for reducing significant supply-side deficits and increasing the efficiency of power generation. The present energy system standard emphasizes lowering the total cost of power without limiting consumption by opting to lower electricity use during peak hours. The previous problem necessitates the development and growth of a flexible and mobile technology that meets the needs of a wide variety of customers while preserving the general energy balance. In order to replace a partial load decrease in a controlled manner, smart energy management systems are designed, according to the preferences of the user, for the situation of a full power loss in a particular region. Smart Energy Management Systems incorporate cost-optimization methods based on human satisfaction with sense input features and time of utilization. In addition to developing an Internet of Things (IoT) for data storage and analytics, reliable LoRa connectivity for residential area networks is also developed. The proposed method is named as LoRa_bidirectional gated recurrent neural network (LoRa_ BiGNN) model which achieves 0.11 and 0.13 of MAE, 0.21 and 0.23 of RMSE, 0.34 and 0.23 of MAPE for heating and cooling loads

Incentive based Residential Demand Aggregation

From the beginning of the twenty-first century, the electrical power industry has moved from traditional power systems toward smart grids. However, with the increasing amount of renewable energy resources integrated into the grid, there is a significant challenge in power system operation due to the intermittency and variability of the renewables. Therefore, the utilization of flexible and controllable demand-side resources to maintain power system efficiency and stability has become a fundamental goal of smart grid initiatives. Meanwhile, due to the development of communication and sensing technologies, intelligent demand-side management with automatic controls enables residential loads to participate in demand response programs. Therefore, the aggregate control of residential appliances is anticipated to be feasible technique in the near future, which will bring considerable benefits to both residential consumers and load-serving entities. Hence, this dissertation proposes a comprehensive optimal framework for incentive based residential demand aggregation. The contents of this dissertation include: 1) a hardware design of smart home energy management system, 2) a new model to assess the responsive residential demand to financial incentives, and 3) an online algorithm for scheduling residential appliances. The proposed framework is expected to generate optimal control strategies over residential appliances enrolled in incentive based DR programs in real time. To residential consumers, this framework will 1) provide easy-to-use smart energy management solution, 2) distribute financial rewards by their quantified contribution in DR events, and 3) maintain residents’ comfort-level expectations based on their energy usage preferences. To LSEs, this framework can 1) aggregate residential demand to enhance system reliability, stability and efficiency, and 2) minimize the total reward costs for executing incentive based DR programs. Since this framework benefits both load serving entities and residents, it can stimulate the potential capability of residential appliances enrolled in incentive based DR programs. Eventually, with the growing number of DR participants, this framework has the potential to be one of the most vital parts in providing effective demand-side ancillary services for the entire power system

Smart home energy management: An analysis of a novel dynamic pricing and demand response aware control algorithm for households with distributed renewable energy generation and storage

Home energy management systems (HEMS) technology can provide a smart and efficient way of optimising energy usage in residential buildings. One of the main goals of the Smart Grid is to achieve Demand Response (DR) by increasing end users’ participation in decision making and increasing the level of awareness that will lead them to manage their energy consumption in an efficient way. This research presents an intelligent HEMS algorithm that manages and controls a range of household appliances with different demand response (DR) limits in an automated way without requiring consumer intervention. In addition, a novel Multiple Users and Load Priority (MULP) scheme is proposed to organise and schedule the list of load priorities in advance for multiple users sharing a house and its appliances. This algorithm focuses on control strategies for controllable loads including air-conditioners, dishwashers, clothes dryers, water heaters, pool pumps and electrical vehicles. Moreover, to investigate the impact on efficiency and reliability of the proposed HEMS algorithm, small-scale renewable energy generation facilities and energy storage systems (ESSs), including batteries and electric vehicles have been incorporated. To achieve this goal, different mathematical optimisation approaches such as linear programming, heuristic methods and genetic algorithms have been applied for optimising the schedule of residential loads using different demand side management and demand response programs as well as optimising the size of a grid connected renewable energy system. Thorough incorporation of a single objective optimisation problem under different system constraints, the proposed algorithm not only reduces the residential energy usage and utility bills, but also determines an optimal scheduling for appliances to minimise any impacts on the level of consumer comfort. To verify the efficiency and robustness of the proposed algorithm a number of simulations were performed under different scenarios. The simulations for load scheduling were carried out over 24 hour periods based on real-time and day ahead electricity prices. The results obtained showed that the proposed MULP scheme resulted in a noticeable decrease in the electricity bill when compared to the other scenarios with no automated scheduling and when a renewable energy system and ESS are not incorporated. Additionally, further simulation results showed that widespread deployment of small scale fixed energy storage and electric vehicle battery storage alongside an intelligent HEMS could enable additional reductions in peak energy usage, and household energy cost. Furthermore, the results also showed that incorporating an optimally designed grid-connected renewable energy system into the proposed HEMS algorithm could significantly reduce household electricity bills, maintain comfort levels, and reduce the environmental footprint. The results of this research are considered to be of great significance as the proposed HEMS approach may help reduce the cost of integrating renewable energy resources into the national grid, which will be reflected in more users adopting these technologies. This in turn will lead to a reduction in the dependence on traditional energy resources that can have negative impacts on the environment. In particular, if a significant proportion of households in a region were to implement the proposed HEMS with the incorporation of small scale storage, then the overall peak demand could be significantly reduced providing great benefits to the grid operator as well as the households

Design of Smart Home Energy Management System for Saving Energy

The rapid change and development in human life, information technology, and the increase in using home gadgets, modern appliances, and electric cars, leads to more dependency on electrical resources and consecutive increase in CO2 emission from generation plant. The current world issue is on how to save the energy by reducing the consumption and decreasing global warming. In this research, Smart Home Energy Management System (SHEMS) has been developed to operate home appliances in an optimum approach. It is aimed at reducing the consumption energy by detecting the residents’ activity and identifying it among three states: Active, Away, or Sleep. The SHEMS is designed with an algorithm that is based on Hidden Markov Model (HMM) in order to estimate the probability of the home being in each of the above states. The proposed system uses the WiFi technology for data transmission inside home and the GSM technology for external communication. The proposed system and its algorithm was successfully tested and 18% of energy saving were obtained

Intelligent Decision Support System for Energy Management in Demand Response Programs and Residential and Industrial Sectors of the Smart Grid

This PhD thesis addresses the complexity of the energy efficiency control problem in residential and industrial customers of Smart electrical Grid, and examines the main factors that affect energy demand, and proposes an intelligent decision support system for applications of demand response. A multi criteria decision making algorithm is combined with a combinatorial optimization technique to assist energy managers to decide whether to participate in demand response programs or obtain energy from distributed energy resources

Changing demand: flexibility of energy practices in households with children (final report)

This report summarises the findings and recommendations from the ‘Changing Demand: Flexibility of energy practices in households with children’ research project funded by the Consumer Advocacy Panel. The project aimed to understand how households with children may be affected by electricity market reforms and demand management initiatives, such as cost-reflective pricing. The study involved 44 in-depth interviews, home tours and observations and a national survey with over 500 Australian households with children. Overview In households with children, many of the practices which use energy are coordinated and concentrated in the late afternoon and early evening on weekdays. Parents’ reliance on routine to manage the demands of family life limits the flexibility of energy use. With limited ability to shift practices to other times of the day, and priorities such as ‘doing what’s best for children’ and ‘using time efficiently’ taking precedence, households with children risk financial disadvantage under pricing strategies such as Time-of-Use (TOU) pricing. Financial insecurity is widespread in, but not limited to, low-income and sole parent households. Health concerns, thermally inefficient housing and appliances, housing tenure, safety and noise concerns, and widespread tariff confusion also restrict the capacity of households with children to manage energy use and costs. Many parents had little time, interest or trust to investigate tariff choice and available energy information. As such, increasing choice and complexity in electricity market offerings does not meet the needs of these households and TOU pricing is unlikely to achieve its aims with this household group. Family routines were more amenable to disruption on an occasional basis for non-financial reasons. For example, 85 per cent of survey respondents said they would reduce electricity use for a ‘peak alert’ in hot weather.  Acting for the ‘common good’ appealed to most parents, for example to prevent an electricity outage and/or be part of a community effort. Household activities considered inflexible for a hypothetical TOU tariff, such as home cooling, television and computer activities and cooking, were considered.  Recommendations from this study include reassessing the energy policy focus on price signals, tariff choice and information to address issues of household demand in Australia. Several alternatives are proposed such as peak alerts, and affordable access to public cooling during hot peak days.&nbsp