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Realistic Scheduling Mechanisms for Smart Homes
Abstract and Figures
Achieving the goal of the decarbonized economy by 2050, the smart grid as future
power networked grid tends to reshape the entire power sector. The transition
from traditional power sources to renewable power generation sources is on its
pace, however, within this transition period, being resourceful in energy consumption
is the only option. This thesis deals with energy management within the
residential sector and is in threefold.
Literature reports the gap between laboratory results and actual results regarding
an Energy Management Systems (EMSs). Hence, there is a need for a numerical
definition of user comfort that can predict the impact of EMS under user
preferences. This problem forms the first part of this thesis i.e., orchestrating a
performance metric regarding the optimum selection of EMS. Based on the comparative
analysis of the state of the art work in Home EMS (HEMS), certain
problems are identified and to overcome these problems a realistic load shifting
mechanism that can reduce the peaks, maximize user convenience and tends to
reduce energy eficiency gap, is modeled afterward. Proposed management system
develops a balance amongst a trade off i.e., cost saving and delay in Time of Use
(ToU) of appliances. Finally, concerning a smart community, an energy management
model is presented inducing the concept of sharing economy without heavy
investments. To achieve this, a highly distributed, intelligent and scalable solution
is presented, proposing the idea of Power Distribution Hub (PDH) to minimize
individual bills and power consumption from utility at crucial hours.
Hence, major contributions in this dissertation are; devising a performance metric
\User Comfort Level (UCL)", formulating a realistic EMS for a smart home and
a realistic power sharing mechanism for a smart community. While, the major
focus is to reduce the use of power from the utility at crucial hours and achieve
financial gains.
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This paper proposes the hybrid approach for multiple distributed generators placement to achieve a high loss reduction in a primary distribution networks. The analytical method has been extended for practical power injections. This approach is based on improved analytical expressions to calculate the optimum size of DGs and heuristic technique to identify the best locations for DG allocations. The optimal power factors of the DGs have also been evaluated in this work. To validate the proposed hybrid approach, results have been compared with particle swarm optimization (PSO) technique and existing fast improved analytical (IA) approach results. The proposed technique has been tested on 33-bus test system.
In this paper, a Multi-agent based locally administrated Power Distribution Hub (PDH) for social welfare is proposed that optimizes energy consumption, allocation and management of Battery Energy Storage Systems (BESSs) for a smart community. Initially, formulation regarding optimum selection of a power storage system for a home (in terms of storage capacity) is presented. Afterwards, the concept of sharing economy is inducted in the community by demonstrating PDH. PDH is composed of multiple small scale BESSs (each owned by community users), which are connected together to form a unified-ESS. Proposed PDH offers a localized switching mechanism that takes decision, whether to buy electricity from utility or use unified-ESS. This decision is based on the price of electricity at “time of use” and “State of Charge” (SoC) of unified-ESS. In response to power use or share, electricity bills are created for individual smart homes by incrementing or decrementing respective sub-meters. There is no buying or selling of power from PDH, there is power sharing with the concept of “no profit no loss”. The objective of proposed PDH is to limit the purchase of electricity on “high priced” hours from the utility. This not only benefits the utility at crucial hours, but also provide effective use of power at the demand side. The proposed Multi-agent System (MAS) depicts the concept of sharing power economy within a community. Finally, the proposed model is analyzed analytically, considering On-Peak, Off-Peak and mid-level (Mid-Peak) prices of a real-time price signal during 24 hours of a day. Results clearly show vital financial benefits of “sharing power economy” for end users and efficient use of power within the smart community.
Demand Response (DR) programs under the umbrella of Demand Side Management (DSM) tend to involve end users in optimizing their Power Consumption (PC) patterns and offer financial incentives to shift the load at “low-priced” hours. However, users have their own preferences of anticipating the amount of consumed electricity. While installing an Energy Management System (EMS), the user must be assured that this investment gives optimum comfort of bill savings, as well as appliance utility considering Time of Use (ToU). Moreover, there is a difference between desired load distribution and optimally-scheduled load across a 24-h time frame for lowering electricity bills. This difference in load usage timings, if it is beyond the tolerance level of a user, increases frustration. The comfort level is a highly variable phenomenon. An EMS giving optimum comfort to one user may not be able to provide the same level of satisfaction to another who has different preferences regarding electricity bill savings or appliance utility. Under such a diversity of human behaviors, it is difficult to select an EMS for an individual user. In this work, a numeric performance metric,“User Comfort Level (UCL)”isformulatedonthebasisofuserpreferencesoncostsaving,toleranceindelayregardinguse of an appliance and return of investment. The proposed framework (UCL) allows the user to select an EMS optimally that suits his.her preferences well by anticipating electricity bill reduction, tolerable delay in ToU of the appliance and return on investment. Furthermore, an extended literature analysis is conducted demonstrating generic strategies of EMSs. Five major building blocks are discussed and a comparative analysis is presented on the basis of the proposed performance metric.
Energy saving is a hot topic due to the proliferation of climate changes and energy challenges globally. However, people's perception about using smart technology for energy saving is still in the concept stage. This means that people talk about environmental awareness readily, yet in reality, they accept to pay the given energy bill. Due to the availability of electricity and its integral role, modulating consumers' attitudes towards energy savings can be a challenge. Notably, the gap in today's smart technology design in smart homes is the understanding of consumers' behaviour and the integration of this understanding into the smart technology. As part of the Paris Climate change agreement (2015), it is paramount for Singapore to introduce smart technologies targeted to reduce energy consumption. This paper focused on the perception of Singapore households on smart technology and its usage to save energy. Areas of current research include: (1) energy consumption in Singapore households, (2) public programs and policies in energy savings, (3) use of technology in energy savings, and (4) household perception of energy savings in smart homes. Furthermore, three case studies are reviewed in relation to smart homes and smart technology, while discussing the maturity of existing solutions.
Purpose of Review
Electric grids face significant challenges with peak and variable demand and greenhouse gas emissions. As new technologies develop, they are used to modernize grids through improved monitoring and management of building electricity use. In this review, a range of technologies are discussed, including the state of their implementation and their current and future potential influence on building electricity contributions.
Recent Findings
Recent literature has focused on the use of these devices individually for modeling building performance, influencing occupant behavioral energy efficiency, and model predictive control for more dynamically operated buildings.
Summary
The results suggest that while smart meters are the most common device, other grid-connected technologies have the potential to further improve monitoring and management of the grid. However, there still remains significant gaps in the literature that require further study to take full advantage of the diversity of connected technologies to achieve a more energy-efficient built environment that can more dynamically consume electricity.
Energy management system Microgrid Optimal operation and scheduling Responsive load demand a b s t r a c t In recent years, increasing interest in developing small-scale fully integrated energy resources in distributed power networks and their production has led to the emergence of smart Microgrids (MG), in particular for distributed renewable energy resources integrated with wind turbine, photovoltaic and energy storage assets. In this paper, a sustainable day-ahead scheduling of the grid-connected home-type Microgrids (H-MG) with the integration of non-dispatchable/dispatchable distributed energy resources and responsive load demand is co-investigated, in particular to study the simultaneously existed uncontrollable and controllable production resources despite the existence of responsive and non-responding loads. An efficient energy management system (EMS) optimization algorithm based on mixed-integer linear programming (MILP) (termed as EMS-MILP) using the GAMS implementation for producing power optimization with minimum hourly power system operational cost and sustainable electricity generation of within a H-MG. The day-ahead scheduling feature of electric power and energy systems shared with renewable resources as a MILP problem characteristic for solving the hourly economic dispatch-constraint unit commitment is also modelled to demonstrate the ability of an EMS-MILP algorithm for a H-MG under realistic technical constraints connected to the upstream grid. Numerical simulations highlight the effectiveness of the proposed algorithmic optimization capabilities for sustainable operations of smart H-MGs connected to a variety of global loads and resources to postulate best power economization. Results demonstrate the effectiveness of the proposed algorithm and show a reduction in the generated power cost by almost 21% in comparison with conventional EMS.
Recently, Home Energy Management (HEM) controllers have been widely used for residential load management in a smart grid. Generally, residential load management aims {to reduce the electricity bills and also curtail the Peak-to-Average Ratio (PAR)}. In this paper, we design a HEM controller on the {basis} of four heuristic algorithms: Bacterial Foraging Optimization Algorithm (BFOA), Genetic Algorithm (GA), Binary Particle Swarm Optimization (BPSO), and Wind Driven Optimization (WDO). Moreover, we proposed {a} hybrid algorithm which is Genetic BPSO (GBPSO). All the selected algorithms are tested with the consideration of essential home appliances in Real Time Pricing (RTP) environment. Simulation results show that each algorithm in the HEM controller reduces the electricity cost and curtails the PAR. GA based HEM controller performs relatively better in term of PAR reduction; it curtails approximately $34\%$ PAR. Similarly, BPSO based HEM controller performs relatively better in term of cost reduction {, as} it reduces approximately $36\%$ cost. Moreover, GBPSO based HEM controller performs better than the other algorithms based HEM controllers in terms of both cost reduction and PAR curtailment.
With the development of home area network, residents have the opportunity to schedule their power usage at the home by themselves aiming at reducing electricity expenses. Moreover, as renewable energy sources are deployed in home, an energy management system needs to consider both energy consumption and generation simultaneously to minimize the energy cost. In this paper, a smart home energy management model has been presented that considers both energy consumption and generation simultaneously. The proposed model arranges the household electrical and thermal appliances for operation such that the monetary expense of a customer is minimized based on the time-varying pricing model. In this model, the home gateway receives the electricity price information as well as the resident desired options in order to efficiently schedule the appliances and shave the peak as well. The scheduling approach is tested on a typical home including variety of home appliances, a small wind turbine, PV panel and a battery over a 24-hour period.
Today, the evolution of smart grid, electric vehicles (EV) with V2G (voltage to the grid) mode, and deployment of renewable energy sources (RES) are bringing revolutionary changes to the existing electrical grid. Volt-VAR optimization (VVO) is a well-studied problem, for bringing solutions to reduce the losses and demand along the distribution lines. The current VVO, however, does not acknowledge the role of elastic and inelastic loads, EVs, and RESs to reduce the reactive power losses and hence the cost of generation. We propose a mathematical model Volt-VAR and CVR Optimization (VVCO)/Optimal Energy Consumption Model (OECM) to solve the VVO problem by considering load shifting, EV as the storage and carrier of the energy, and use of RES. The VVCO/OECM not only reduces the reactive load but also flatten the load curve to reduce the uncertainty in the generation and to decrease the cost. The system also considers the efficiency of the electrical equipment to enhance the lifetime of the devices. We develop a non-cooperative game to solve the VVCO/OECM problem. To evaluate the performance, we simulate the VVCO/OECM model and compare with the existing VVO solution. We found that our method took almost a constant time to produce a solution of VVO regardless of the size of the network. The proposed method also outperform the existing VVO solution by reducing the generation cost and flatten the load and minimizes the uncertainty in the power generation. Results have shown that exploiting RES will reduce the voltage drop through reducing the injection of reactive power to the system.
