A multi-agent intelligent decision making support system for home energy management in smart grid: A fuzzy TOPSIS approach

In the context of intelligent home energy management in smart grid, the occupants' consumption behavior has a direct effect on the demand and supply of the electrical energy market. Correspondingly, the policies of the utility providers affect consumption behavior so techniques and tools are required to analyse the occupants' preferences, habits and lifestyles in order to support and facilitate their decision-making regarding the curtailing of their energy consumption and costs. The uncertainty about householders' preferences increases the uncertainty of appliance prioritization and makes it difficult to determine the consistency of preferences in terms of energy consumption. In this complex system, the preferences and judgments of householders are represented by linguistic and vague patterns. This paper proposes a much better representation of this linguistics that can be developed and refined by using the evaluation methods of fuzzy set theory. The proposed approach will apply the fuzzy Technique for Order Preference by Similarity to Ideal Solution (fuzzy TOPSIS) for achieving preferences. Based on our detailed literature review of the multi-agent system approach in this field, it is expected that the proposal model will offer a robust tool for communication and decision-making between occupant agents and dynamic environmental variables. It is shown that the proposed fuzzy TOPSIS approach will enable and assist householders to maximize their participation in demand response programs

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

Development of robust building energy demand-side control strategy under uncertainty

The potential of carbon emission regulations applied to an individual building will encourage building owners to purchase utility-provided green power or to employ onsite renewable energy generation. As both cases are based on intermittent renewable energy sources, demand side control is a fundamental precondition for maximizing the effectiveness of using renewable energy sources. Such control leads to a reduction in peak demand and/or in energy demand variability, therefore, such reduction in the demand profile eventually enhances the efficiency of an erratic supply of renewable energy. The combined operation of active thermal energy storage and passive building thermal mass has shown substantial improvement in demand-side control performance when compared to current state-of-the-art demand-side control measures. Specifically, "model-based" optimal control for this operation has the potential to significantly increase performance and bring economic advantages. However, due to the uncertainty in certain operating conditions in the field its control effectiveness could be diminished and/or seriously damaged, which results in poor performance. This dissertation pursues improvements of current demand-side controls under uncertainty by proposing a robust supervisory demand-side control strategy that is designed to be immune from uncertainty and perform consistently under uncertain conditions. Uniqueness and superiority of the proposed robust demand-side controls are found as below: a. It is developed based on fundamental studies about uncertainty and a systematic approach to uncertainty analysis. b. It reduces variability of performance under varied conditions, and thus avoids the worst case scenario. c. It is reactive in cases of critical "discrepancies" observed caused by the unpredictable uncertainty that typically scenario uncertainty imposes, and thus it increases control efficiency. This is obtainable by means of i) multi-source composition of weather forecasts including both historical archive and online sources and ii) adaptive Multiple model-based controls (MMC) to mitigate detrimental impacts of varying scenario uncertainties. The proposed robust demand-side control strategy verifies its outstanding demand-side control performance in varied and non-indigenous conditions compared to the existing control strategies including deterministic optimal controls. This result reemphasizes importance of the demand-side control for a building in the global carbon economy. It also demonstrates a capability of risk management of the proposed robust demand-side controls in highly uncertain situations, which eventually attains the maximum benefit in both theoretical and practical perspectives.Ph.D.Committee Chair: Augenbroe, Gofried; Committee Member: Brown, Jason; Committee Member: Jeter, Sheldon; Committee Member: Paredis,Christiaan; Committee Member: Sastry, Chellur

Residential Demand Side Management model, optimization and future perspective: A review

The residential load sector plays a vital role in terms of its impact on overall power balance, stability, and efficient power management. However, the load dynamics of the energy demand of residential users are always nonlinear, uncontrollable, and inelastic concerning power grid regulation and management. The integration of distributed generations (DGs) and advancement of information and communication technology (ICT) even though handles the related issues and challenges up to some extent, till the flexibility, energy management and scheduling with better planning are necessary for the residential sector to achieve better grid stability and efficiency. To address these issues, it is indispensable to analyze the demand-side management (DSM) for the complex residential sector considering various operational constraints, objectives, identifying various factors that affect better planning, scheduling, and management, to project the key features of various approaches and possible future research directions. This review has been done based on the related literature to focus on modeling, optimization methods, major objectives, system operation constraints, dominating factors impacting overall system operation, and possible solutions enhancing residential DSM operation. Gaps in future research and possible prospects have been discussed briefly to give a proper insight into the current implementation of DSM. This extensive review of residential DSM will help all the researchers in this area to innovate better energy management strategies and reduce the effect of system uncertainties, variations, and constraints

Upscaling energy control from building to districts: current limitations and future perspectives

Due to the complexity and increasing decentralisation of the energy infrastructure, as well as growing penetration of renewable generation and proliferation of energy prosumers, the way in which energy consumption in buildings is managed must change. Buildings need to be considered as active participants in a complex and wider district-level energy landscape. To achieve this, the authors argue the need for a new generation of energy control systems capable of adapting to near real-time environmental conditions while maximising the use of renewables and minimising energy demand within a district environment. This will be enabled by cloud-based demand-response strategies through advanced data analytics and optimisation, underpinned by semantic data models as demonstrated by the Computational Urban Sustainability Platform, CUSP, prototype presented in this paper. The growing popularity of time of use tariffs and smart, IoT connected devices offer opportunities for Energy Service Companies, ESCo’s, to play a significant role in this new energy landscape. They could provide energy management and cost savings for adaptable users, while meeting energy and CO2 reduction targets. The paper provides a critical review and agenda setting perspective for energy management in buildings and beyond

Computer Aided Home Energy Management system

High prices are associated with the peak electricity demand and thus, price can be used as an indicator of power system condition in the peak load management programs. This paper investigates the potential of peak load management based on price-responsive load control for the residential sector. The Computer Aided Home Energy Management (CAHEM) system controls residential demand in response to the hourly market data including price, load and temperature data. A fuzzy demand controller incorporates customer preferences in determining operational settings of residential appliances. A prototype CAHEM system is demonstrated using X10 home networking technology. The aggregate level effects of the CAHEM system on peak load reduction are simulated for the Pennsylvania-New Jersey-Maryland market during the summer of 1999. The study also estimates the optimal level of large-scale adoption of the CAHEM system

Strategic initiatives to increase the uptake of rooftop photovoltaic systems

This thesis has focused on the strategies that can be implemented by electricity utilities and private investors in increasing the penetration of rooftop photovoltaic systems (RPVs). Even though the proposals are general and applicable for any locality, the key studies of this research have been focused on the Australian electricity market. First, a detailed review and comparison of all Australian power distribution companies has been carried out in terms of the percentage of the supplied customers and the customer density per kilometre length of their power lines. Following that, the daily electricity supply and the electricity unit charges offered by the active electricity retail companies in the zones of each of these power distribution companies are reviewed and compared. Based on this information, the annual electricity bill of a customer supplied by different power distribution companies and retailers is calculated. Through this study, the national average annual electricity bill has been determined for Australia and the power distribution companies are categorised under four segments of very cheap, cheap, expensive, and very expensive companies. This study has highlighted some of the key challenges faced by power distribution companies in Australia in supplying power through a more localised renewable based generation. Installing an RPV by a household is a big decision, and there are many factors which need to be considered before this decision. It can be highly rewarding in some cases and for others, it may bring a loss in the investment. The main factors which need to be considered are the electricity consumption tariff, electricity consumption pattern, the location of the household and the tariffs offered by the utility in that area. In this thesis, economic incentives of installing a RPV and battery energy storage (BES) are discussed for a household in different states, served by various utilities. A comparison is made to find which states are more suitable in terms of gaining financial benefits from RPVs. A flat rate feed-in tariff is an incentive offered by many utilities to encourage their customers to invest in electricity generation from RPVs. Such a scheme is usually designed by financial techniques that mostly consider the initial capital cost and electricity spot price. However, such an incentive cannot help the utilities to address the technical challenges in networks with large renewable penetration. In this thesis, a dynamic feed-in tariff has been proposed and designed based on the value of electricity, hosting capacity, ambient temperature and time of day. This feed-in tariff will specifically support utilities that experience challenges in the electrification of remote areas or observe excessive stress on their networks at demand peak periods. The proposed feed-in tariff encourages the rural customers to install RPVs while discouraging the urban customers from installing RPVs without BES. Solar leasing is another opportunity to enhance the rapid uptake of RPVs. Even though solar leasing has attracted widespread acceptance in some countries, it has not been successful in being popular in some other places mainly due to lack of awareness of the model and economic viability in relation to outright buying a RPVs. One of the solar leasing models is roof rental in which a company leases the roof of residential premises for installing RPVs and selling the generated electricity to the utility. This thesis has explored an economically viable alternative for roof rental from the perspective of the engaged leasing company. To this end, an economic analysis has been performed to determine the net present value from the roof rental payments and versus different ratings of RPVs, desired interest rate and existing feed-in tariff. Furthermore, a BES can play an important role in realising maximum benefit from RPVs. However, the cost of a BES is comparatively high, and the BES of individual households may not be optimally utilised during a significant portion of the year as there may not be enough generation from RPVs during winter to charge the BES to its full capacity. Community solar on the other hand, if optimally designed, can give the opportunity to use a BES to its maximum capacity. Such systems can benefit many of the remote and rural communities, that are usually supplied by diesel generators, or long traditional distribution lines, which in addition to being expensive often don’t provide the reliability at desired level. These systems can also benefit most of the urban areas since the unmanaged penetration of RPVs has resulted in the undesired duck curve profile in the network. To this end, this thesis has proposed and validated the appropriate design criteria for community solar projects with an aim to improve the network duck curve profile, enable peak-shaving and increase the self-sufficiency of the community

Identification and characterization of irregular consumptions of load data

The historical information of loadings on substation helps in evaluation of size of photovoltaic (PV) generation and energy storages for peak shaving and distribution system upgrade deferral. A method, based on consumption data, is proposed to separate the unusual consumption and to form the clusters of similar regular consumption. The method does optimal partition of the load pattern data into core points and border points, high and less dense regions, respectively. The local outlier factor, which does not require fixed probability distribution of data and statistical measures, ranks the unusual consumptions on only the border points, which are a few percent of the complete data. The suggested method finds the optimal or close to optimal number of clusters of similar shape of load patterns to detect regular peak and valley load demands on different days. Furthermore, identification and characterization of features pertaining to unusual consumptions in load pattern data have been done on border points only. The effectiveness of the proposed method and characterization is tested on two practical distribution systems